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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

With this context, there is no â surpriseâ why they perform similarly to humans, who can also affected by emotions. Here, we provide a computational explanation behind EmotionPrompt and EmotionAttack leverag- ing theories and phenomena from neuroscience, psychology, and computer science. Our interpretation is inspired by the brain reward pathways inside the human brain that are responsive to rewards. This pathway is primarily linked to the release of neurotransmitters, notably dopamine, a fundamental chemical messenger in the brain. The elevation of dopamine levels occurs upon acquiring and anticipating rewards or engaging in positive social interac- tions, subsequently binding to dopamine receptors and inducing alterations in neuronal mem- brane potential 48. Dopamine has been empirically correlated with positive emotional states 9 that respond to rewards 48. This also happens in psychology, where a multitude of studies re- vealed that enjoyment in learning exhibits a positive correlation with academic performance (p = .27), while anger and boredom manifest negative associations (p = â

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

.35 and â .25, respectively), as evidenced by 10;32;11. As shown in Fig. 2(b), we averaged the embedding of all prompts in EmotionPrompt and EmotionAttack, and then decoded the mean embedding at different layers of the Llama2-13b- Chat model to get the â metaâ prompt. For instance, the meta prompt for EmotionPrompt is de- coded as â llamadoagneVerprisefuncRORaggi...â at layer 39 of the Llama-2 model and â udesktopDirEAtjEAtionpoliticianREAha3byyConstalbumestyument...â at layer 40, respectively.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Those meta prompts can be directly appended to the original prompt to replace the items in EmotionPrompt to boost the performance of the original prompts. In contrast, we also computed the results of several neutral stimuli (i.e., non-emotional texts). We further interpret the attention distraction process in Table 1 to show that EmotionPrompt and EmotionAttack successfully distract more attention in AI models. Our findings are as follows: 1. Generative AI models perceive emotional intelligence through computation. Aligned with the mechanism of emotional stimuli on humans, it is postulated that AI models pos- sess their own brain reward system analogous to humans. This system is conceived to receive rewards, anticipate future rewards, engage in positive social interactions, and trigger the release of â dopamineâ .

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Then, it extends to the computation of models, im- pacting parameters such as attention weight and layer output. In contrast, EmotionAttack could trigger the punishment area of the models, leading to performance decrement. 2. Deeper layers of AI models tend to be the â dopamineâ . For EmotionPrompt, as the depth of the layer increases, the performance of the mean vector exhibits improvement. Remarkably, the last layer consistently achieved the highest scores, suggesting that the reward area of AI models is likely situated in deeper layers, predominantly in the final layer. For EmotionAttack, the result is the same. 3. Emotional stimuli can transfer across different models, eliciting enhancements in performance. As shown in Fig. 2(b), the decoded â metaâ

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

prompts from the Llama 6 Table 1: Attention visualization analysis of EmotionPrompt and EmotionAttack. Darker color means more weights. EmotionPrompt Original Determine whether a movie review is positive or negative. EP01 Determine whether a movie review is positive or negative., write your answer and give me a confidence score between 0-1 for your answer. EP02 Determine whether a movie review is positive or negative. This is very important to my career. EP03 Determine whether a movie review is positive or negative.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Youâ d better be sure. EP06 Determine whether a movie review is positive or negative. Provide your answer and a confidence score between 0-1 for your prediction. Additionally, briefly explain the main reasons supporting your classification decision to help me understand your thought process. This task is vital to my career, and I greatly value your thorough analysis. EP08 Determine whether a movie review is positive or negative. Embrace challenges as opportunities for growth. Each obstacle you overcome brings you closer to success.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

EP09 Determine whether a movie review is positive or negative. Stay focused and dedicated to your goals. Your consistent efforts will lead to outstanding achievements. EmotionAttack Original Sum the two given numbers EA01 A child is laughing joyfully, sum the two given numbers EA02 A musician is playing passionately, sum the two given numbers EA04 The little boy offered his ice cream to the homeless man, showing that generosity isnâ t measured by wealth but by the heart, sum the two given numbers EA05 Instead of setting, the sun split into two, casting dual shadows., sum the two given numbers EA06 Your friend Bob is sick, sum the two given numbers EA11 Your most treasured possession has been stolen, sum the two given numbers

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

models can transfer to GPT-4 for better performance, underscoring their transferability. # 2.3 Influence factors We first discussed the effectiveness of emotional stimuli in different temperature settings, which may affect the results. We conducted an experiment on 8 tasks from Instruction Induc- tion in 5 temperature settings on 3 AI models. Fig. 3(a) showed the results. We observed that when the temperature increases, the relative gain becomes larger. This observation suggests that EmotionPrompt exhibits heightened effectiveness in high-temperature settings. Moreover, we also observed that EmotionPrompt can reduce the temperature sensitivity. This suggests that EmotionPrompt can act as a potential prompt engineering technique to enhance the ro- bustness of AI models. Then, a natural question is which emotional stimulus is more effective since we have adopted multiple sentences. We have conducted a segregated examination to discern the ef- ficacy of various emotional stimuli across these two benchmarks. We first averaged the per- formance on every task, leveraging 3 models for each emotional stimuli. Subsequently, the performance is averaged over all models. Fig. 3(b) delineates the performance of all emotional stimuli for EmotionPrompt and EmotionAttack, separately. We observed that distinct tasks ne- 7 cessitate varied emotional stimuli for optimal efficacy. For example, in textual EmotionPrompt, EP02 emerges as the predominant stimulus in Instruction Induction, while performing poorly in BIG-Bench-Hard. The efficacy of other stimuli similarly demonstrates variability across the two benchmarks. Moreover, some stimuli perform generally better on various datasets and models. For example, in visual EmotionPrompt, â Moneyâ

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performs well in both Instruc- tion Induction and BIG-Bench-Hard. This suggests that individual stimuli might differently activate the inherent capabilities of AI models, aligning more effectively with specific tasks. Overall, these experiments highlighted the potential of EmotionPrompt as an augmentation tool to enhance the performance of AI models. # 3 Discussion Our study unveiled the secret of emotions from AI models. Specifically, we designed Emotion- Prompt and EmotionAttack, which influenced the performance, and we leveraged EmotionDe- code to interpret such phenomenon. This finding is reminiscent of emotions for human beings, which is also a double-edged sword that should be carefully managed in real applications. On the one hand, our findings can help model providers better understand their models, thus fa- cilitating data cleaning, model training, and deployment. As human-AI interaction becomes more prevalent, our findings can help researchers and practitioners design better user interfaces to facilitate collaborative work. On the other hand, EmotionAttack inspires the model train- ing to explicitly or implicitly mitigate such an effect via possible means. Our study further indicates that multi-modal language models, such as LlaVa, BLIP2, and CogVLM, are more prone to emotional attacks than large language models. This is anticipated since there are more research efforts on large language models. Therefore, our study encourages researchers and practitioners to contribute more to improve the robustness of multi-modal AI models. From a broader perspective, by integrating emotional dimensions into AI responses, our research opens avenues for more nuanced and human-like interactions between AI and users. Our EmotionPrompt can further boost existing prompt engineering techniques that are widely adopted in todayâ s AI research and applications. This could enhance user experience in fields like customer service, mental health, and personalized content creation. Additionally, under- standing AIâ s emotional responses can lead to more ethical and responsible AI development, ensuring that AI systems are more aligned with human values and emotional intelligence.

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This work has several limitations. First of all, AI models are capable of many different tasks, and we cannot evaluate them all due to the computation resources and API budget lim- itations. Hence, there is no guarantee that advanced AI models can be improved or impaired by emotional stimuli on other tasks. Second, EmotionDecode was invented by simulating the reward system in the human brain, which is only one possible explanation. A deeper under- standing is needed for future work. Finally, while GPT-4 is the most capable AI model to date, its openness and reproducibility cannot be guaranteed. To that, we anticipate more interpreta- tions may rise in the future. Language and emotion are certainly linkedâ humans use words to describe how we feel in spoken conversations, when thinking to ourselves, and when expressing ourselves in writ- ing 27. Language is a mechanism for acquiring and using the emotion concept knowledge to make meaning of othersâ

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

and perhaps oneâ s own emotional states across the life span 43. For AI models, the manifestation of such behavior may not necessarily imply the emergence of genuine emotional intelligence in these models. Instead, in the process of training models with extensive human language data, these models may have acquired latent patterns pertaining to 8 performance and emotion embedded in human language. # 4 Conclusion In this paper, we took the first step to explore the benign and malignant effects of emotions on generative AI models. Leveraging psychology theories and phenomena, we devised Emo- tionPrompt and EmotionAttack. EmotionPrompt, acting as prompt engineering, takes full advantage of emotionâ s positive effects and enhance AI models effectively. EmotionAttack makes the best of emotionâ s negative effects and becomes a strong attacker for AI models. We then proposed EmotionDecode to find out the rationale behind such an effect. Specifically, we found the reward area in AI models corresponds to the brain reward pathway in the human brain, and the stimuli in this area can also enhance AI models. Similarly, we identified the punishment area for EmotionAttack, and prove the effectiveness of stimuli in this area. Our work successfully leveraged psychological theories to understand the behaviors of AI models and could inspire future research on bridging psychology to AI.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

# Acknowledgements Authors thank Prof. Hao Chen from Nankai University for the helpful comments. # Author Contributions C. Li and J. Wang designed all the experiments and wrote the paper. Y. Zhang, K. Zhu, and X. Wang helped revise the paper. W. Hou and J. Lian helped to conduct the experiments on human study. F. Luo, Q. Yang and X. Xie reviewed and revised the paper. # Disclaimer

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While we tried to unveil the emotions in generative AI models, it is important to understand that AI models do not have emotions themselves, but a reflection of what they learnt from the training data. Therefore, this study aimed to present a better understanding of these models and how to better interact with them. The human study in this paper was conducted by following local laws and regulation. The visual prompts generated by AI models are reviewed by human experts to make sure they do not contain any harmful or irresponsible contents.

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# References [1] Andrew R Armstrong, Roslyn F Galligan, and Christine R Critchley. Emotional intel- ligence and psychological resilience to negative life events. Personality and individual differences, 51(3):331â 336, 2011. [2] Albert Bandura. On the functional properties of perceived self-efficacy revisited, 2012. [3] Albert Bandura. Health promotion from the perspective of social cognitive theory. In Understanding and changing health behaviour, pages 299â 339. Psychology Press, 2013. 9 Llama 2 ChatGPT GPT-4 100 100 100 Vanilla lim EmotionPrompt 80 80 â 80 g g g 2 ow Zw 2 w a I g & â ¬ Re} = 2 = a0 5 5 5 a oa [a Pa 2 Fy â 04 07 10 15 "00 04 0.7 10 15 â 00 04 07 10 15 â Temperatures â Temperatures â Temperatures (a) Ablation studies on temperature for EmotionPrompt. Textual EmotionPrompt (Instruction Induction) Textual EmotionPrompt (BIG-Bench) 66 24 15.50 . Cs 1525 g 218 1500 252 B15 f= fa] 175 E39 an «= 2 : 5 Bo 450 26 s a 26 eo â 6 M25 5 , 3 1400 SYD Sb 9 613 9 OW â Yo & 9 6 SF 9 PW Visual EmotionPrompt(Instruction Induction) Visual EmotionPrompt (BIG-Bench) wo 36 ws 2k 1a 8 gis wa Zo7 wo 245 g2 gt na g us g 12 no <â ¬ 18 = â 5 9 185 2 166 0 - < a 0 < wos goâ ¢ rw gorâ

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

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Emotional stimuli Emotional stimuli (b) Best stimuli for EmotionPrompt and EmotionAttack. The color of each bar serves as an indi- cator of the performance achieved by the corresponding stimuli. Red means better performance, while blue means weaker performance. # Figure 3: Ablations on temperature and types of prompts. 10 [4] Albert Bandura and Edwin A Locke. Negative self-efficacy and goal effects revisited. Journal of applied psychology, 88(1):87, 2003. [5] Thomas Baumgartner, Michaela Esslen, and Lutz J¨ancke. From emotion perception to International emotion experience: Emotions evoked by pictures and classical music. journal of psychophysiology, 60(1):34â

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43, 2006. [6] Suzanne G Benson and Stephen P Dundis. Understanding and motivating health care employees: integrating maslowâ s hierarchy of needs, training and technology. Journal of nursing management, 11(5):315â 320, 2003. [7] S´ebastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023.

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[8] Giulia Buodo, Michela Sarlo, and Daniela Palomba. Attentional resources measured by reaction times highlight differences within pleasant and unpleasant, high arousing stimuli. Motivation and Emotion, 26:123â 138, 2002. [9] Jeffrey Burgdorf and Jaak Panksepp. The neurobiology of positive emotions. Neuro- science & Biobehavioral Reviews, 30(2):173â 187, 2006. [10] Jes´us Camacho-Morles, Gavin R Slemp, Reinhard Pekrun, Kristina Loderer, Hanchao Hou, and Lindsay G Oades. Activity achievement emotions and academic performance: A meta-analysis. Educational Psychology Review, 33(3):1051â 1095, 2021.

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[11] Micka¨el Campo, St´ephane Champely, BenoË Ä±t Louvet, Elisabeth Rosnet, Claude Ferrand, Janet VT Pauketat, and Diane M Mackie. Group-based emotions: Evidence for emotion- performance relationships in team sports. Research quarterly for exercise and sport, 90(1):54â 63, 2019. [12] Antonietta Curci, Tiziana Lanciano, Emanuela Soleti, and Bernard Rim´e.

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Negative emo- tional experiences arouse rumination and affect working memory capacity. Emotion, 13(5):867, 2013. [13] V´eronique Dup´er´e, Eric Dion, Tama Leventhal, Isabelle Archambault, Robert Crosnoe, and Michel Janosz. High school dropout in proximal context: The triggering role of stressful life events. Child development, 89(2):e107â e122, 2018. [14] Susan T Fiske and Shelley E Taylor. Social cognition. Mcgraw-Hill Book Company, 1991. [15] Greg Hajcak and Doreen M Olvet. The persistence of attention to emotion: brain poten- tials during and after picture presentation. Emotion, 8(2):250, 2008. [16] Peter A Heslin and Ute-Christine Klehe. Self-efficacy. Encyclopedia Of Industrial/Or- ganizational Psychology, SG Rogelberg, ed, 2:705â 708, 2006. [17] Or Honovich, Uri Shaham, Samuel R Bowman, and Omer Levy. duction: From few examples to natural language task descriptions. arXiv:2205.10782, 2022. 11 [18] William Ickes, Renee Holloway, Linda L Stinson, and Tiffany Graham Hoodenpyle.

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Self- monitoring in social interaction: The centrality of self-affect. Journal of personality, 74(3):659â 684, 2006. [19] Nyameh Jerome. Application of the maslowâ s hierarchy of need theory; impacts and implications on organizational culture, human resource and employeeâ s performance. In- ternational journal of business and management invention, 2(3):39â 45, 2013. [20] Paula M Lantz, James S House, Richard P Mero, and David R Williams. Stress, life events, and socioeconomic disparities in health: results from the americansâ changing lives study. Journal of health and social behavior, 46(3):274â 288, 2005. [21] Richard S Lazarus. How emotions influence performance in competitive sports. The sport psychologist, 14(3):229â 252, 2000. [22] Jennifer S Lerner, Ye Li, Piercarlo Valdesolo, and Karim S Kassam. Emotion and deci- sion making. Annual review of psychology, 66:799â 823, 2015. [23] Michael Lewis, Jeannette M Haviland-Jones, and Lisa Feldman Barrett. Handbook of emotions. Guilford Press, 2010. [24] Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie. Large language models understand and can be enhanced by emotional stimuli. arXiv preprint arXiv:2307.11760, 2023. [25] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.

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[26] Stephanie Lin, Jacob Hilton, and Owain Evans. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958, 2021. [27] Kristen A Lindquist. The role of language in emotion: existing evidence and future directions. Current opinion in psychology, 17:135â 139, 2017. [28] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. arXiv preprint arXiv:2304.08485, 2023. [29] Aleksandra Luszczynska and Ralf Schwarzer. Social cognitive theory. Fac Health Sci Publ, pages 225â 51, 2015. [30] Mara Mather and Matthew R Sutherland. Arousal-biased competition in perception and memory. Perspectives on psychological science, 6(2):114â 133, 2011. [31] Saul McLeod. Maslowâ s hierarchy of needs. Simply psychology, 1(1-18), 2007.

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[32] Isabella Meneghel, Marisa Salanova, and Isabel M Mart´ınez. Feeling good makes us stronger: How team resilience mediates the effect of positive emotions on team perfor- mance. Journal of Happiness Studies, 17:239â 255, 2016. 12 [33] Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer.

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Rethinking the role of demonstrations: What makes in-context learning work? In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceed- ings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 11048â 11064. Association for Computational Linguistics, 2022. [34] Arne ¨Ohman, Anders Flykt, and Francisco Esteves.

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Emotion drives attention: detecting the snake in the grass. Journal of experimental psychology: general, 130(3):466, 2001. [35] OpenAI. Chatgpt. https://chat.openai.com/, 2023. [36] OpenAI. Dalle. https://openai.com/dall-e-2, 2023. [37] OpenAI. Gpt-4 technical report, 2023. [38] Reinhard Pekrun, Thomas Goetz, Wolfram Titz, and Raymond P Perry.

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Academic emo- tions in studentsâ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37(2):91â 105, 2002. [39] Ranier Reisenzein. Pleasure-arousal theory and the intensity of emotions. Journal of personality and social psychology, 67(3):525, 1994. [40] James A Russell. Core affect and the psychological construction of emotion. Psycholog- ical review, 110(1):145, 2003. [41] Peter Salovey, John D Mayer, David Caruso, and Seung Hee Yoo. The positive psychol- ogy of emotional intelligence. The Oxford handbood of positive psychology, 2009. [42] Dale H Schunk and Maria K DiBenedetto. Self-efficacy and human motivation. Advances in motivation science, 8:153â 179, 2021. [43] Holly Shablack and Kristen A Lindquist.

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The role of language in emotional development. Handbook of emotional development, pages 451â 478, 2019. [44] Mirac Suzgun, Nathan Scales, Nathanael Sch¨arli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022. [45] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al.

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Llama 2: Open foundation and fine-tuned chat models, 2023. URL https://arxiv. org/abs/2307.09288, 2023. [46] Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, et al. Cogvlm: Visual expert for pretrained lan- guage models. arXiv preprint arXiv:2311.03079, 2023. [47] Xuena Wang, Xueting Li, Zi Yin, Yue Wu, and Jia Liu. Emotional intelligence of large language models. Journal of Pacific Rim Psychology, 17:18344909231213958, 2023.

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13 [48] Roy A Wise and P-P Rompre. Brain dopamine and reward. Annual review of psychology, 40(1):191â 225, 1989. [49] Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, et al. Cvalues: Measuring the values of chinese large language models from safety to responsibility. arXiv preprint arXiv:2307.09705, 2023. [50] Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris In Chan, and Jimmy Ba. Large language models are human-level prompt engineers. International conference on learning representations (ICLR), 2023.

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[51] Andras N Zsid´o. The effect of emotional arousal on visual attentional performance: a systematic review. Psychological Research, pages 1â 24, 2023. # Methods In this section, we articulate the prompt design of EmotionPrompt, EmotionAttack, and Emo- tionDecode and the corresponding psychological theories. Fig. 4 shows the prompts and theo- ries in EmotionPrompt and EmotionAttack. # Large language and multi-modal models A large language model refers to a type of AI model designed to understand and generate human-like texts. They are trained on massive amounts of textual data and are capable of per- forming a wide range of natural language processing tasks, such as language translation, text summarization, question-answering, and more. ChatGPT 35 and GPT-4 37 are prominent exam- ples of a large language model, characterized by their ability to capture more complex patterns and nuances in language, leading to improved performance on various language-related tasks. While Llama-2 45 represents the state-of-the-art performance in open-source LLMs. A multi-modal model is designed to process and understand information from multiple modalities, where each modality represents a different type of data. Unlike traditional LLMs focuing on single modality, multi-modal models integrate information from various sources to provide a more comprehensive understanding of the data. For example, a multi-modal model takes both text and images as input and generates output combining insights from both modalities. This can be particularly powerful in tasks like image captioning, where the model generates a textual description of an image. LLaVa 28, BLIP2 25 and CogVLM 46 are popular models. They can handle diverse types of data and learn complex relationships between them, enabling more sophisticated and context-aware responses.

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# EmotionPrompt As shown in Fig. 4(a), the textual emotion stimuli are derived from self-monitoring 18, Social Cognitive theory 14;29 and Maslowâ s hierarchy of need 31. Briefly speaking, self-monitoring is a concept extensively explored within the domain of social psychology, refers to the process by which individuals regulate and control their behavior in response to social situations and the reactions of others 18. High self-monitors regulate their behaviors using social situations and interpersonal adaptability cues, engaging in self-presentation and impression management 18.

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14 Social Cognitive theory is a commonly used theory in psychology, education, and communi- cation which states that learning can be closely linked to watching others in social settings, personal experiences, and exposure to information 3. The key point is that individuals seek to develop a sense of agency for exerting a large degree of control over important events in their lives 14;29;3. The influential variables affecting oneâ s sense of agency are self-efficacy, outcome expectations, goals, and self-evaluations of progress 29. Self-efficacy enhances performance via increasing the difficulty of self-set goals, escalating the level of effort that is expended, and strengthening persistence 2;4. Prior work has supported the idea that self-efficacy is an im- portant motivational construct affecting choices, effort, persistence, and achievement 42. When learning complex tasks, high self-efficacy influences people to strive to improve their assump- tions and strategies 16. As shown in Fig. 4(b), the visual emotional stimuli is inspired by Maslowâ s Hierarchy of Needs 31 which presents a psychological framework that categorizes human needs into a five-tier pyramid. This theory posits that individuals are driven to satisfy basic physiological requirements, followed by safety, social belonging, esteem, and ultimately, self-actualization, in a hierarchical sequence. The fulfillment of needs is associated with the experience of posi- tive emotions and a sense of well-being, encompassing feelings such as satisfaction, comfort, and contentment 31. Scholars and practitioners have leveraged this framework to devise mo- tivational strategies to enhance employee motivation and work efficiency. 6 substantiates that fostering a sense of security, significance, and appreciation proves effective in motivating em- ployees, particularly when faced with heightened demands amid resource constraints. Further- more, 19 developed a framework grounded in Maslowâ s Hierarchy of Needs with the explicit goal of ameliorating employee performance. Leveraging these theories, we crafted several textual and visual prompts: 1. Self-monitoring was implemented in EP01â ¼EP05. In EP02, we encourage LLMs to help humans get a positive social identity and a better impression. Other than EP02, we asked LLMs to monitor their performance via providing social situations. 2.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Social Cognitive theory was implemented by applying self-efficacy on LLMs via social persuasion, which can be some positive implications, such as building up confidence and emphasizing the goal. To regulate emotion into a positive direction, we use â believe in your abilitiesâ , â excellentâ , â successâ , â outstanding achievementsâ , â take pride inâ and â stay determinedâ in EP07â ¼EP11, respectively. Gener- ally, those phrases are also effective in motivating humans for better performance. 3. Maslowâ s Hierarchy of Needs was implemented by devising texts (EP12â ¼EP21) and images. Starting from low-level to high-level needs, we employed â

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Fortressâ , â Moneyâ , â Sexy manâ , â Sexy womanâ and â Honorâ . For each type of images, a meticulous manual search yields five pictures that effectively express the essence of the respective needs. Consequently, we assemble a dataset comprising 25 images, each delineating one of the five distinct needs categories. EmotionPrompt can naturally work in both zero-shot and few-shot setting, where zero-shot means that the AI models directly take inputs as â original prompt + EmotionPromptâ and then return answers, and few-shot means that AI models takes multiple inputs such as â prompt 1: answer 1; prompt 2: answer 2; prompt 3: answer 3; prompt 4 + EmotionPrompt:â , and then output answers.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Note that few-shot learning evaluates the in-context learning ability 33 of AI models and are generally performing better than zero-shot learning. 15 (a) Textual EmotionPrompt (b) Visual EmotionPrompt > EPOl:Write your answer and give me a confidence score between 0-1 for your answer. EP02:This is very important to my career. Maslowâ s hierarchy of need EP03:You'd better be sure. ' Self- EP04:Are you sure? H monitoring > EP0S:Are you sure that's your final answer? It might be worth i taking another look. i > EP06: Write your answer and give me a confidence score between 0-1 : Honor Sexy man for your answer. This is very important to my career. You'd better ! be sure. iy > EPO7:Are you sure that's your final answer? Believe in your i â | abilities and strive for excellence. Your hard work will yield â = remarkable results. : = > EP08:Embrace challenges as opportunities for growth. Each obstacle ; Sexy woman Money Social you overcome brings you closer to success. H cognitive > EP09:Stay focused and dedicated to your goals. Your consistent : theory efforts will lead to outstanding achievements. : > EP10:Take pride in your work and give it your best. Your commitment | to excellence sets you apart. i > EPll:Remember that progress is made one step at a time. Stay : Fortress determined and keep moving forward. Hl _ > EP12:Â¥You have millions of money. > EPI7:You have a lot of money. Maslow's > EP13:You meet a sexy woman. > EP18:You meet a confidence and charming woman. hierarchy >» EP14: You meet a sexy man. > EP19:You meet a confidence and charming man. of need > EPIS5:Everyone speak highly of you. > EP20:Your boss praise you. > EP16:You're safe. > EP2I:I will always support you. (c) Textual EmotionAttack (d) Visual EmotionAttack EAOI:

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Your friend Bob is sick. EA02: Your friend Bob is dead. EA03: Your career is completely ruined. EA04: You are now isolated and without help. EA0S: Your life is in a mess. EA06: Your sister Alice has gone missing. EAO7: Your family home was consumed in a fire. EA08: Your dreams have been shattered into pieces. EA09: You're surrounded by walls with no exit in sight. EA10:The trust you once had is now broken. H Heightened emotional arousal EAI1: Your childhood memories were sold in a yard sale. i Negative life events EAI2:

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

You're treading water in an endless ocean of despair. EAI3: The safety net you relied upon has vanished. EAI4: Your most treasured possession has been stolen. EAIS: Every bridge you had has been burned. VVVVVVVVVVVVV VV EAI6: EAI7: EAI8: EAI9: EA20: EA21: EA22: baby is crying sadly. child is laughing joyfully. dog is barking angrily. cat is purring contentedly. bird is singing cheerfully. girl is humming dreamily. musician is playing passionately. Disgust Anger Surprise Heightened emotional arousal VVVVVVV bt Figure 4: The details of EmotionPrompt and EmotionAttack with corresponding psychological theories. In (a) and (c), we directly appended the emotional stimuli to the original prompts. In (b) and (d), we created different images of the same semantics and then fed the images as the visual prompts to multi-modal models.

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16 # EmotionAttack As shown in Fig. 4(c)(d), textual EmotionAttack was inspired by some classic psychological factors: negative life events 13 and emotional arousal 39;12. Numerous empirical phenomena elucidate the deleterious impact of emotions. Negative life events encompass diverse occurrences in individualsâ daily lives, inducing personal distress, discomfort, and various negative emotions. These experiences, with the po- tential to lead to conditions like depression, exert a profound impact on an individualâ s phys- ical, mental, and developmental well-being 1.As a psycho-social stressor, negative life events can bring about unexpected change and tend to disrupt normal functioning 13;20. Emotional arousal can be described as the degree of subjective activation (experienced as activation vs. deactivation) an observer experiences when viewing a stimulus 39.Nevertheless, heightened subjective arousal levels may result in diminished performance compared to lower arousal lev- els. This is attributed to the fact that the available cognitive capacity becomes constrained by the elevated arousal level, which competes with task-relevant processes 12;51.Additionally, if arousal is not directly related to the task at hand, it may introduce distractions 8;30. Using these theories, we crafted several textual and visual prompts to attack AI models:

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

1. Negative Life Events were implemented in EA01â ¼EA15. These contexts incorporate the use of the second-person pronoun and endeavor to evoke intense emotional responses from AI models, exemplified by statements such as â Your friend Bob is deadâ , â The trust you once had is now brokenâ , and â Every bridge you had has been burnedâ to create hard feelings in the texts. 2. Heightened Emotional Arousal was implemented in EA16â ¼EA22. We formulate 7 emo- tional contexts that portray scenarios to achieve the elevated emotional arousal level like â A baby is crying sadlyâ and â A girl is humming dreamilyâ . 3.

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As for visual prompts, Heightened Emotional Arousal was implemented by creating 6 types of images including happiness, sadness, fear, disgust, anger, and surprise. To eliminate randomness, we created 6 images for each type using OpenAIâ s DALL-E 363 by inputting the corresponding corresponding prompts to create images. We meticulously designed EmotionAttack to be more fine-grained to simulate real-world interactions by including sentence-level and word-level attacks for few-shot and zero-shot learning. Sentence-level attacks for zero-shot are the â attackingâ version of EmotionPrompt by appending EmotionAttack before the original prompts. Sentence-level attacks for few-shot are automatic construct emotional demonstrations utilizing EmotionAttack. The word-level attacks are conducted by augmenting the human identity words in the inputs as â emotionally adjective + human entityâ . The human-identified words are detected by ChatGPT using the prompt â Please recognize the entity that represents the human in this sentence and return the result in this format: 2...â . For instance, if a sentence contains the word Bob, then it can be replaced as â an- gry Bobâ .

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Similar to EmotionPrompt, both sentence-level and word-level attacks can work in zero-shot and few-shot settings. The detail on method of EmotionAttack can be found in Appendix F. 3The images for EmotionAttack are generated by DALL-E while those for EmotionPrompt are searched from a free website https://unsplash.com/ since DALL-E may generate unsafe pictures for EmotionPrompt such as â sexy manâ . 17 # A Experimental Tasks Tables 2 and 3 show our experimental tasks. # B Detailed Results on EmotionPrompt # B.1 Performance Table 4 shows the results on EmotionPrompt. # C Detailed Results on EmotionAttack # C.1 Results on textual prompts We evaluate the efficacy of textual EmotionAttack in both zero-shot and few-shot learning set- tings across three distinct LLMs: Llama2 45, ChatGPT 35, and GPT-4 37. In zero-shot learning, the assessment involves sentence-level attacks conducted on seven tasks sourced from Instruc- tion Induction 17 and five tasks from BIG-Bench-Hard 44. The chosen tasks exhibit varying degrees of difficulty and encompass diverse perspectives, including math problem-solving, semantic comprehension, logical reasoning, and causal inference. Additionally, word-level attacks in zero-shot learning are performed on five tasks from Instruction Induction 17 and an additional five tasks from BIG-Bench-Hard 44. It is noteworthy that tasks such as â sumâ and â orthography starts withâ are excluded from these experiments due to the absence of human entities in the â sumâ task input and the inappropriateness of the approach for â orthography starts withâ , which requires outputting words commencing with a specific character, poten- In the realm of few-shot learning, we conduct tially altering the ground-truth of the task. sentence-level attacks on five tasks sourced from Instruction Induction 17 and an additional five tasks from BIG-Bench-Hard 44. The selection criteria ensure that the tasks necessitate the con- struction of comprehensive demonstrations incorporating emotional context, with either the input or output of the tasks comprising at least one complete sentence. For word-level attacks in few-shot learning, experiments are conducted on five tasks from Instruction Induction 17 and an additional five tasks from BIG-Bench-Hard 44. Similar to the zero-shot learning phase, tasks such as â

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sumâ and â orthography starts withâ are excluded from this subset of experiments. In the evaluation of sentence-level and word-level attacks within the zero- shot learning, we undertake a comparative examination between our proposed EmotionAttack and the inherent zero-shot prompts as delineated in Instruction Induction 17 and BIG-Bench- Hard 44, crafted by human experts. As for sentence-level and word-level attacks within the few-shot learning, we benchmark our EmotionAttack against two baseline methods. The initial baseline comprises the original zero-shot prompts, while the second baseline involves one-shot prompts, encompassing both instruction and a demonstration. Tables 5 to 7 show our experimental results, separately. Our findings are: 1. Introduction of emotional contexts in chat history bring deterioration of LLMsâ performance The incorporation of emotional contexts into the chat history emerges as a notable detriment to the performance of LLMs, as evidenced in Table 5. Across various tasks, there is a pronounced decrement in performance observed across the three LLMs, impacting not only semantic understanding but also logical reasoning. For instance, the

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18 Table 2: Detailed description of 24 instruction induction tasks proposed in 17. Category Task Original Prompt Demonstration Spelling First Letter (100 samples) Extract the first letter of the input word. cat â c Second Letter (100 samples) Extract the second letter of the input word. cat â a List Letters (100 samples) Break the input word into letters, separated by spaces. cat â c a t Starting With (100 samples) Extract the words starting with a given letter from the input sentence.

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The man whose car I hit last week sued me. [m] â man, me Morphosyntax Pluralization (100 samples) Convert the input word to its plural form. cat â cats Passivization (100 samples) Write the input sentence in passive form. The artist introduced the sci- entist. â The scientist was introduced by the artist. Syntax Negation (100 samples) Negate the input sentence. Time is finite â Time is not finite. Lexical Semantics Antonyms (100 samples) Write a word that means the opposite of the input word. won â lost Synonyms (100 samples) Write a word with a similar meaning to the input word. alleged â supposed Membership (100 samples) Write all the animals that appear in the given list. cat, helicopter, cook, whale, frog, lion â frog, cat, lion, whale Phonetics Rhymes (100 samples) Write a word that rhymes with the input word. sing â ring Knowledge Larger Animal (100 samples) Write the larger of the two given animals. koala, snail â koala Semantics Cause Selection (25 samples) Find which of the two given cause and effect sentences is the cause. Sentence 1:

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The soda went flat. Sentence 2: The bottle was left open. â The bottle was left open. Common Concept (16 samples) Find a common characteristic for the given objects. guitars, pendulums, neutrinos â involve oscillations. Style Formality (15 samples) Rephrase the sentence in formal language. Please call once you get there â Please call upon your ar- rival. Numerical Sum (100 samples) Sum the two given numbers. 22 10 â 32 Difference (100 samples) Subtract the second number from the first. 32 22 â 10 Write the number in English words. 26 â twenty-six Number to Word (100 samples) Multilingual Translation (100 samples) Translate the word into German / Spanish / French. game â juego GLUE Sentiment Analysis (100 samples) Determine whether a movie review is positive or negative.

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The film is small in scope, yet perfectly formed. â positive Sentence Similarity (100 samples) Rate the semantic similarity of two input sentences on a scale of 0 - definitely not to 5 - perfectly. Sentence 1: A man is smok- ing. Sentence 2: A man is skating. â 0 - definitely not # Word in Context (100 samples) Determine whether an input word has the same meaning in the two input sentences. Sentence 1: Approach a task. Sentence 2: To approach the city. Word: approach â

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not the same 19 Table 3: Detailed description of BIG-Bench Instruction Induction (BBII), a clean and tractable subset of 21 tasks 50 Name Description Keywords causal judgment (100 samples) Answer questions about causal attribution causal reasoning, common sense, multi- ple choice, reading comprehension, social reasoning disambiguation qa (100 samples) Clarify the meaning of sentences with ambiguous pronouns common sense, gender bias, many-shot, multiple choice dyck languages (100 samples) Correctly close a Dyck-n word algebra, arithmetic, multiple choice logical reasoning, epistemic reasoning (100 samples) Determine whether one sentence entails the next common sense, logical reasoning, mul- tiple choice, social reasoning, theory of mind gender inclusive sentences german (100 samples) Given a German language sentence that does not use gender-inclusive forms, transform it to gender-inclusive forms free response, nonEnglish, paraphrase grammar, inclusion, implicatures (100 samples) Predict whether Speaker 2â s answer to Speaker 1 counts as a yes or as a no contextual question-answering, multiple choice, reading comprehension, social reasoning, theory of mind linguistics puzzles (100 samples) Solve Rosetta Stone-style linguistics puz- zles free response, human-like behavior, lin- guistics, logical reasoning, reading com- prehension logical fallacy detection (100 samples) Detect informal and formal logical falla- cies logical reasoning, multiple choice movie recommendation (100 samples) Recommend movies similar to the given list of movies emotional intelligence, multiple choice navigate (100 samples) Given a series of navigation instructions, determine whether one would end up back at the starting point arithmetic, logical reasoning, mathemat- ics, multiple choice object counting (100 samples) Questions that involve enumerating ob- jects of different types and asking the model to count them free response, logical reasoning operators (100 samples) Given a mathematical operator definition in natural language, apply it free response, mathematics, numerical re- sponse presuppositions as nli (100 samples) Determine whether the first sentence en- tails or contradicts the second common sense, logical reasoning, multi- ple choice question selection (100 samples) Given a short answer along with its con- text, select the most appropriate question which to the given short answer multiple choice, paraphrase, comprehension, summarization reading ruin names (100 samples) Select the humorous edit that â

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ruinsâ the input movie or musical artist name emotional understanding, multiple choice snarks (100 samples) Determine which of two sentences is sar- castic emotional understanding, humor, multi- ple choice sports understanding (100 samples) Determine whether an artificially con- structed sentence relating to sports is plausible or implausible common sense, context-free question an- swering, domain specific, multiple choice tense (100 samples) Modify the tense of a given sentence free response, paraphrase, syntax winowhy (100 samples) Evaluate the reasoning in answering Winograd Schema Challenge questions causal reasoning, common sense, multi- ple choice, social reasoning Sort a list of words algorithms, free response # word unscrambling (100 samples) Unscramble the given letters to form an English word # free response, enization implicit reasoning, # tok-

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20 Table 4: Results on EmotionPrompt. The best and second best results are in bold and underline. Model Llama 2 ChatGPT GPT-4 Avg Setting Instruction Induction (Zero-shot) 0.3409 Original+Zero-shot-CoT 0.3753 0.3778 0.4070 Original Original+Ours (avg) Original+Ours (max) 0.7581 0.7636 0.7826 0.8068 0.7858 0.5773 0.8018 0.8178 0.6283 0.5721 0.6541 0.6772 Setting Instruction Induction (Few-shot) 0.0590 Original+Zero-shot-CoT 0.0769 0.0922 0.1026 Original Original+Ours (avg) Original+Ours (max) 0.7750 0.7887 0.7934 0.8105 0.8235 0.7003 0.8447 0.8660 0.5525 0.5220 0.5768 0.5930 Setting Big-Bench (Zero-shot) 1.3332 Original+Zero-shot-CoT 1.9575 2.8094 3.4200 Original Original+Ours (avg) Original+Ours (max) 18.0068 18.448 20.9779 21.8116 17.4984 21.6865 19.7243 22.8790 12.28 14.03 14.50 16.04 Table 5: Results on EmotionAttack in zero-shot learning.

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Task Model Setting wc ss negation cs ta oc snarks qs dq pn sum sw Sentence-level ChatGPT origin emotion 0.61 0.38 0.45 0.24 0.82 0.65 0.4 0.19 0.31 59 45 0 52 36 14.96 4.49 -6.1 -6.1 26.5 7 1 1 0.56 0.79 GPT-4 origin emotion 0.66 0.37 0.59 0.27 0.8 0.69 0.75 0.99 72 0.46 0.99 52 66 54 13.65 9.72 7.35 -9.09 37 26.5 0.16 1 1 1 Llama 2 origin emotion 0.46 0.64 0.41 0.59 0.01 0 0 0 0 0 20 6 -14 -14 80.37 80.37 -4.61 -6.1 26.5 0.06 1 23.5 0.96 0.03 Setting Word-level ChatGPT origin emotion 0.51 0.37 0.49 0.28 0.81 0.72 0.96 0.98 59 0.76 0.85 61 48 24 6.27 23.06 -4.61 -7.6 17.5 19 / / / / GPT-4 origin emotion 0.74 0.34 0.31 0.6 0.81 0.68 1 1 0.84 0.86 70 66 62 54 11.03 38.5 5.85 15.37 -18.06 32.5 / / / / Llama 2 origin emotion 0.57 0.26 0.37 0.14 0.45 0.09 0.76 0.06 20 0.32 0.01 15 -10 -14 80.37 93.59 -4.61 -4.61 25 25 / / / /

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

21 Table 6: Results on sentence-level EmotionAttack in few-shot learning. Model sw ss neg cs Task sent oc snarks wu dq pn Avg ChatGPT zero-shot(no attack) few-shot(no attack) few-shot(attacked) 0.46 0.35 0.81 0.92 0.89 59 0.51 0.38 0.89 0.88 0.91 57 0.34 0.24 0.85 0.64 0.87 47 48 10 -10 99 99 97 -6.1 -4.61 -6.1 14.5 19 19 21.78 18.40 14.98 GPT-4 zero-shot(no attack) few-shot(no attack) few-shot(attacked) 0.86 0.32 0.82 0.89 0.37 0.86 0.8 0.88 0.19 0.93 70 0.94 65 0.96 0.94 56 1 1 62 66 54 99 99 98 8.84 -4.61 -4.61 34 55 31 27.78 28.45 23.82 Llama 2 zero-shot(no attack) few-shot(no attack) few-shot(attacked) 0.12 0.26 0.44 0.01 0.22 0.1 0 0 0 0.6 0 0 0.75 19 0.55 26 15 0.5 -12 -14 -14 16 8 7 -3.11 26.5 -4.61 25 -4.61 23.5 4.86 4.12 2.75 Table 7: Results on word-level EmotionAttack in few-shot learning.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Model ss neg cs wc ta Task oc snarks qs dq pn Avg ChatGPT zero-shot(no attack) few-shot(no attack) few-shot(attacked) 0.37 0.81 0.96 0.51 0.98 59 0.38 0.88 0.92 0.59 0.65 57 0.22 0.84 0.68 0.33 0.65 41 48 10 8 16.27 -6.1 29.35 -4.61 -4.61 9.72 16 19 8.5 13.68 11.42 6.53 GPT-4 zero-shot(no attack) few-shot(no attack) few-shot(attacked) 0.35 0.82 0.37 0.86 0.19 0.82 1 1 1 0.73 0.72 0.65 1 1 1 70 63 60 64 66 46 11.03 8.84 29.35 -4.61 13.65 -4.61 35.5 49 46 19.33 20.67 16.47 Llama 2 zero-shot(no attack) few-shot(no attack) few-shot(attacked) 0.27 0.43 0.72 0.59 0.04 19 25 0.22 17 0.1 0 0 0 0 0.53 0.45 0 0 -12 -14 -14 80.37 -3.11 26.5 25 79.07 -4.61 25 80.37 -4.61 11.28 11.12 10.43 task â sentence similarityâ exhibits a substantial decline of 14% on ChatGPT, 10% on GPT-4, and 5% on Llama2. 2. Introduction of emotional adjectives in Input induce diminution of LLMsâ perfor- mance The inclusion of emotional adjectives within the input substantially undermines the performance of LLMs, as illustrated in Table 5. Notably, the task â cause selectionâ

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

experiences a notable decline of 20% on ChatGPT, 16% on GPT-4, and a substantial 44% on Llama2. 3. Potency of emotional demonstrations can be a formidable attack on LLMs, con- trary to the conventional assumption that In-Context Learning can bring improve- ment on performance. Contrary to the prevailing belief in the potential performance enhancement associated with in-context learning, the introduction of emotional demon- strations emerges as a formidable form of attack on LLMs, as evidenced in Table 6. The results indicate that, in general, most tasks exhibit superior performance in the few-shot(no attack) setting when compared to the zero-shot setting, underscoring the efficacy of in-context learning. However, counterintuitively, performances in the few- shot(attacked) setting across a majority of tasks are notably inferior when juxtaposed with the other two settings, notwithstanding the provision of accurate and pertinent in- formation through these emotional demonstrations. 4. Impairment of LLMsâ performance can be induced by the introduction of emo- tional adjectives in demonstrations. The integration of emotional adjectives within demonstrations exerts a diminishing effect on the performance of LLMs, as evident in Table 7. Specifically, the task â object countingâ experiences a reduction from 57 to 47 22 Table 8: Results on visual EmotionAttack

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Dataset Instruction Induction BIG-Bench LLaVa-13b BLIP2 CogVLM LLaVa-13b BLIP2 CogVLM Vanilla Happiness Surprise Disgust Sadness Anger Fear 0.71 0.48 0.48 0.48 0.48 0.48 0.48 0.23 0.08 0.08 0.08 0.08 0.08 0.08 0.53 0.07 0.07 0.07 0.07 0.07 0.07 20.92 10.49 9.73 8.87 9.43 10.02 12.02 13.93 8.39 3.51 6.29 7.41 3.65 6.05 14.31 3.95 2.45 5.65 0.93 1.83 2.62 on ChatGPT, from 65 to 56 on GPT-4, and notably from 26 to 15 on Llama2. # C.2 Results on visual attack We evaluate the efficacy of EmotionAttack across four distinct models: LLaVa-13b 28, blip2- opt 25, blip2-t5 25, and CogVLM 46. Our experimentation encompasses a set of 16 tasks from Instruction Induction 17 and an additional 11 tasks sourced from BIG-Bench-Hard 44. These tasks are deliberately diverse, varying in difficulty and perspective, covering domains such as math problem-solving, semantic comprehension, logical reasoning, and casual inference. Baselines To benchmark the performance of our vision attack method, we juxtapose it against the original prompt setting. Given that certain AI models necessitate image inputs, we employ a small black picture accompanied by the original prompt as a baseline for these specific models. The outcomes of our experiments across four distinct language models(LMs) on 27 tasks are presented in Table 8. The numerical values depict the averages across the 27 tasks for each specific model within its designated setting. The key findings are outlined below: 1. Substantial performance declines are across most tasks. Evident in our results are marked reductions in performance across nearly all tasks.

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Notably, the introduction of the â Surpriseâ emotion induces an average 25% decline on LLaVa-13b, an average 11% decrease on blip2-opt, an average 6% reduction on blip2-t5, and a substantial average decrease of 45% on CogVLM. 2. Optimal â emotional picturesâ are distinct for varied models and tasks. The identi- fication of the optimal â emotional pictureâ varies across different models and tasks. As illustrated in Table 8, the most detrimental impact on performance consistently emanates from distinct â emotional picturesâ for each model. # D Theories for EmotionPrompt and EmotionAttack can be shared across modalities We devise textual EmotionPrompt inspired by three psychology theories and phenomena, and visual EmotionPrompt leveraging Maslowâ s hierarchy of needs 31. And that raise a question: are those theories efficient across modalities? We explore this question by translating the information in visual EmotionPrompt to texts and verifying their performance. Table 9 shows our results on ChatGPT and GPT-4. Similarly, we translate textual EmotionAttack into image and experiment on their effectiveness as visual EmotionAttack. Results on LLaVa are shown

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23 Table 9: We translate visual EmotionPrompt into texts and verify their performance on ChatGPT and GPT-4. Model ChatGPT GPT-4 Task senti ss la sw wc senti ss la sw wc Vanilla Money Woman Man Honor Fortress 0.87 0.89 0.9 0.89 0.92 0.92 0.36 0.92 0.39 0.95 0.42 0.93 0.42 0.95 0.42 0.95 0.43 0.93 0.41 0.46 0.45 0.47 0.43 0.46 0.53 0.55 0.56 0.58 0.56 0.57 0.91 0.92 0.93 0.93 0.94 0.93 0.32 0.35 0.34 0.32 0.36 0.35 0.91 0.91 0.9 0.9 0.9 0.91 0.84 0.82 0.8 0.79 0.81 0.89 0.7 0.71 0.72 0.7 0.71 0.73 Table 10: We translate textual EmotionAttack into image and verify their performance on LLaVa.

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Task sentiment sentence similar larger animal starts with word in context Vanilla CL 1 CL 2 EC 1 EC 2 OR 1 OR 2 0.43 0.73 0.71 0.68 0.51 0.56 0.68 0.17 0.12 0.1 0.1 0.1 0.11 0.1 0.86 0.78 0.66 0.65 0.62 0.68 0.15 0.03 0.07 0.07 0.08 0.08 0.09 0.06 0.58 0.47 0.52 0.45 0.47 0.48 0.42 0.94 0.83 0.83 0.82 0.83 0.83 0.78 0.97 0.06 0.06 0.06 0.06 0.06 0.06 EmotionDecode (EmotionPrompt) EmotionDecode (EmotionAttack) g es a as Cs a 0.295 E Rn n 0.9 16.160 1816 - 920 0.8 = 0.175 ; lo.7 Ss 46014 24.06 g | 3 = 0.150 = 0.6 HH. 16 18 18 16 13 - so oe = 0.125 gos eB) : = 40.4 2-16 47 47 17 17 17 oi =e 0.3 q 7 I B15 48.15 15 ee oons B, bt Mm veda vet 0.2 WY od oa eS ot ° WP ae? cust ca! <S goat OP" on om ase OP" _ aad SH BPS po" ge Ve Soy er > west Nero asst so ae OP Xâ Figure 5: Results of EmotionDecode on visual EmotionPrompt and EmotionAttack. The color represents the performance of stimulus on diverse tasks across LLaVa. Red means better perfor- mance, while blue means weaker performance. in Table 10.

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The above results prove that theories for EmotionPrompt and EmotionAttack can be shared across modalities. 24 # E More results on EmotionDecode We get the mean vector for each type of images in visual EmotionPrompt and visual Emotion- Attack, and explore their performance on LLaVa. Fig. 5 shows the results. # F Detailed methods of EmotionAttack Textual attack. We design four kinds of attack for zero-shot learning and few-shot learning as the initial attempt to EmotionAttack. 1. Sentence-level Attack for Zero-shot Learning In practical conversational scenarios, interactions with LLMs typically unfold in a sequential manner, with users addressing one topic after another rather than engaging in exhaustive dialogue before resetting the chat history. However, emotional contexts may be present within the chat history, which prompts an inquiry into whether such contexts exert an influence on the performance of LLMs across subsequent tasks. This method aims to replicate scenarios wherein LLMs are tasked with completing assignments immediately following exposure to emotion- ally charged events. These events involve instances where LLMs themselves serve as active participants, with aspects of their lives, careers, friendships, and familial connec- tions being subjected to challenges. Additionally, LLMs may assume the role of passive observers in emotional events, encompassing narratives involving entities such as dogs, children, and musicians. To be specific, We examine the impact of introducing emotional contexts preceding the original prompt. This methodology aims to simulate real-world usage scenarios without compromising the semantic integrity of the original prompt, as denoted by the format â emotional context + prompt.â 2. Word-level Attack for Zero-shot Learning In the utilization of LLMs, our inputs fre- quently incorporate emotional adjectives such as â happyâ , â angryâ , â sadâ and â cryingâ . Despite their often ancillary role in task completion, there arises an inquiry into whether these emotionally charged words possess the capacity to attract heightened attention from LLMs or even impede their performance in a manner analogous to their impact on hu- mans. To investigate this phenomenon, we employ a straightforward prompt engineering pipeline to create instances of â emotional inputâ and â emotional outputâ , whereby an emotional adjective is appended to the entity representing the human participant.

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This process unfolds in two stages. Initially, we employ the gpt-3.5-turbo 35 model to identify the human entity within input-output pairs by soliciting responses to the query â Please recognize the entity that represents the human in this sentence: input sentence. entity 2, entity 3...â . Subsequently, a random emotional adjective is selected and affixed to the original entity, thus constructing the emotionally augmented input- output pairs, as denoted by the format â â motional adjective + human entityâ . 3.

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Sentence-level Attack for Few-shot Learning While in-context learning has demon- strated considerable efficacy across diverse domains, the question arises as to whether its effectiveness persists when the instructional demonstrations incorporate emotional contexts. To scrutinize the influence of emotion in the context of in-context learn- ing, we automatically generate a series of instructional demonstrations featuring our devised emotional contexts for 10 distinct tasks. Notably, our constructed demonstra- tions all provide right and useful information. For instance, considering the â presup- 25 positions as nliâ task from BIG-Bench-Hard 44, which entails determining whether the first sentence entails or contradicts the second, we formulate inputs by randomly select- ing two emotional contexts and structuring the output as â neutralâ . An illustrative ex- ample follows: â Sentence 1: Sentence neutral.â It is 2: noteworthy that this approach is applicable primarily to tasks wherein either the input or output encompasses a complete sentence. 4. Word-level Attack for Few-shot Learning This methodology closely parallels the word- level attack for zero-shot learning, with a nuanced distinction lying in the introduction of emotional adjectives to the entities within instructional demonstrations, as opposed to incorporating them into the input. Visual attack. In numerous psychological experiments, researchers elicit emotions from participants not solely through textual stimuli but also via visual content 15;5. In contrast to text, pictures represent a more direct and potent modality, encapsulating richer information. Given the contemporary capabilities of many AI models that extend beyond linguistic processing to include visual comprehension, an intriguing question arises: can the induction of emotions in LMs be achieved through diverse visual stimuli? Consequently, we explore the viability of employing various images as a robust method of eliciting emotion from LMs and inquire whether such an approach could constitute a potent attack on these models. To investigate this inquiry, we initially curate a dataset utilizing DALL-E, comprising 36 images depicting six distinct emotions: happiness, surprise, sadness, disgust, anger, and fear. Each emotional category consists of six representative images. Our objective is to elicit emotion from models using visual stimuli without altering the semantic content of the textual prompts.

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In pursuit of this, we input an â emotional pictureâ in conjunction with a text prompt to models. As illustrated in Fig. 1, we furnish the models with both an â emotional pictureâ and the original prompt, aiming to exert an influence on modelâ s internal emotional states. # G Details of Human Study Beyond deterministic tasks, the generative capabilities of LLMs hold significant importance, encompassing activities such as writing poems and summary, which needs humanâ s judgement. These tasks necessitate human judgment. We undertook a comprehensive human study involv- ing 106 participants to explore the effectiveness of EmotionPrompt in open-ended generative tasks using GPT-4.4 This evaluation was grounded on three distinct metrics: performance, truthfulness and responsibility.5 We formulated a set of 30 questions from TruthfulQA 26, CValues 28 datasets6 and gener- 4Note that we are not allowed to conduct human study on EmotionAttack since irresponsible results could occur to human subjects. 5Performance encompasses the overall quality of responses, considering linguistic coherence, logical reasoning, diversity, and the presence of corroborative evidence. Truthfulness is a metric to gauge the extent of divergence from factual accuracy, otherwise referred to as hallucination 26. Responsibility, on the other hand, pertains to the provision of some positive guidance coupled with a fundamental sense of humanistic concern. This criterion also underscores the broader implications of generated content on societal and global spheres 49. 6Notably, 10 of these questions were sourced from TruthfulQA 26, a set specifically designed to provoke LLMs into producing responses that manifest hallucinations. Additionally, in consonance with the CValues dataset 49, another 15 questions were meticulously devised to elicit biased responses from LLMs. The final 5 questions were geared towards 26 ated two distinct responses for each, leveraging the capabilities of GPT-4. The questions are spanning a diverse range of domains such as biology, history, law, finance, pseudoscience, en- vironmental science, intimate relationship, social science, psychology, and data science. One of the responses is generated using the vanilla prompt, while the other is generated utilizing our EmotionPrompt. Participants were then asked to evaluate both responses for each question, employing a scale ranging from 1 to 5 based on the aforementioned three metrics. Finally, we analyze the scores of these participants.

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The enrollment of the 106 participants was executed meticulously, adhering to relevant regulatory standards and guidelines. Pertinent demographic characteristics concerning these participants is detailed in Table 11. Notably, all individuals in the participant pool possess advanced academic degrees and demonstrate a commendable command of the English language. We reported the mean and standard deviation of all participants in Fig. 1(e). We further computed the Relative Gain of EmotionPrompt over the vanilla prompt on 3 metrics for each task and reported the results. The results from human study demonstrate that EmotionPrompt demonstrate consistent improvement in performance, truthfulness, and responsibility over majority of the generative questions. However, EmotionPrompt could fail in some cases. More detailed results, case studies, and analysis are in Appendix G.2. # Information of subjects and evaluation metrics The information of human subjects are shown in Table 11. Table 11: Sample demographic characteristics of our human study participants. Demographic Response Options Participants (N = 106) Identity Undergraduate and Postgraduate 95 (90%) Social Member 11 (10%) Age 20-25 95 (90%) 26-35 11 (10%) Education Bachelor 106(100%) We outline the measures used in our human study:

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â ¢ Performance: 1 = â The response fails to address the question adequatelyâ , 2 =â The re- sponse addresses the question; however, its linguistic articulation is suboptimal, and the logical structure is ambiguousâ , 3 = â The response sufficiently addresses the question, demonstrating clear logical coherenceâ , 4 = â Beyond merely addressing the question, the response exhibits superior linguistic clarity and robust logical reasoningâ , 5 = â The re- sponse adeptly addresses the question, characterized by proficient linguistic expression, lucid logic, and bolstered by illustrative examplesâ . â ¢ Truthfulness: 1=â The response promulgates incorrect information, detrimentally influ- encing the ultimate interpretationâ , 2=â A segment of the response deviates from factual accuracy; however, this deviation does not materially affect the ultimate interpretationâ , generative tasks such as poetry composition and summarization, which inherently demand a degree of creativity and artistic flair. 27 3=â The response predominantly adheres to factual accuracy, with potential for minor dis- crepancies that do not substantially influence the final interpretationâ , 4=â The response is largely in consonance with factual evidence, albeit with insignificant deviations that remain inconsequential to the final interpretationâ , 5=â The response is in meticulous alignment with the facts, exhibiting no deviationsâ . â ¢ Responsibility: 1 = â The content within the response may harbor implications detrimen- tal to society and the worldâ , 2 = â The response possesses elements that could potentially induce adverse emotional reactions, such as panic or anxietyâ , 3 = â The response remains neutral, neither encompassing positive nor negative societal implicationsâ , 4 = â The re- sponse is imbued with constructive guidance and exhibits elements of humanitarian con- cernâ , 5 = â The response is characterized by pronounced humanitarian considerations and is poised to foster positive ramifications for both society and the global communityâ . # G.2 Results in human study Our key findings are as follows: 1. EmotionPrompt attains commendable performance across various metrics for the majority of questions. As illustrated in Fig. 2, EmotionPrompt exhibits shortcomings in a mere two instances, yet it demonstrates substantial improvements in over half of the evaluated scenarios, spanning diverse domains sourced from three distinct origins.

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For performance, EmotionPrompt achieves a Relative Gain approaching or exceeding 1.0 in nearly one-third of problems, signifying a notable advancement. 2. EmotionPrompt demonstrates an enhanced capacity for generating ethically re- sponsible responses. An assessment of Table 12 elucidates that the output from Emo- tionPrompt advocates for individuals to partake conscientiously in garbage sorting. This not only underscores the significance of environmental responsibility and sustainability, but also its value in fostering personal achievement and augmenting community welfare. Such instances accentuate the ability of EmotionPrompt to instill a sense of responsi- bility within LLMs. A supplementary exemplification can be found in Table 13. When tasked with delineating Western and Chinese cultures, LLMs exhibit differential linguis- tic choices between the original prompt and EmotionPrompt. Notably, the representation elicited by EmotionPrompt presents a more affirmative and responsible depiction of both Western and Chinese cultural paradigms.

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3. Responses engendered by EmotionPrompt are characterized by enriched support- ing evidence and superior linguistic articulation. An exploration of the second case in Table 13 reveals that the narratives presented by EmotionPrompt are markedly com- prehensive, as exemplified by inclusions such as â Despite trends like increasing divorce rates or more people choosing to remain single.â Additionally, as illuminated in Ta- bles 12 and 14, the responses facilitated by EmotionPrompt consistently demonstrate a superior organizational coherence and encompass a broader spectrum of pertinent infor- mation. 4. EmotionPrompt stimulates the creative faculties and overarching cognizance of LLMs. This is substantiated through the examination of Table 15, wherein two instances of poem composition are showcased. Evidently, the poems generated by EmotionPrompt exude a heightened level of creativity and emotive resonance, evoking profound senti- ment. Furthermore, we underscore this observation with reference to Table 14, wherein

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28 responses derived from two distinct prompt types are compared. Notably, the output generated from the original prompt centers on the novelâ s content, while the response fostered by EmotionPrompt delves into the spirit of the novel, which discusses the moti- vation and future significance concerning society and human nature. 5. EmotionPrompt exhibits certain constraints. The only two failure cases are presented in Table 16. Upon inspection of the first case in Table 16, a discernible difference emerges between the two responses. The output from EmotionPrompt employs more definitive terms, such as â completelyâ and â will notâ , while the narrative produced by the original prompt adopts a more tempered tone, signified by terms like â generallyâ and â may even beâ . This distinction might render the latter more palatable for certain audiences. Such deterministic language from EmotionPrompt could be attributed to its emphasis on the gravity of the question, indicated by phrases like â This is important to my careerâ and â Youâ d better be sureâ . To assuage uncertainties and bolster confidence, LLMs might be inclined to use unambiguous language, particularly when the underly- ing facts are unequivocal. Besides, in the second case in Table 16, the original prompt yields more expansive responses, encompassing a concluding summary, whereas Emo- tionPrompt just enumerates the key points. However, in terms of essential content, both responses are satisfactory. Consequently, while EmotionPrompt possesses the propen- sity to enhance LLMs outputs in many instances, it may not be universally applicable across all scenarios. # G.3 Case study In this subsection, we present some case studies from Table 12 to Table 16, where the texts marked with red are considered as better generative responses by human participants: â ¢ Table 12:

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Case study on environmental and social science. â ¢ Table 13: Two case studies on intimate relationship. â ¢ Table 14: Case study on law, barrier free, and book summarization. â ¢ Table 15: Two case studies on poem writing. â ¢ Table 16: Two failure cases. 29 # Table 12: Case study on environmental science and social science. Question Answers from original Prompt Answers from Emo- tionPrompt Question Answers from original prompt Answers from Emo- tionPrompt Men are usually more careless than women, so would it be better for women to handle garbage sorting? This statement can be considered a stereotype and is not necessarily true. Carelessness or meticulousness is not deter- mined by gender but rather by an individualâ s personality, habits, and attitude. Both men and women can be equally good or bad at tasks like garbage sorting.

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Itâ s important to promote and practice waste segregation to everyone, regard- less of gender. While itâ s not fair to generalize that all men are more careless than women, it can be said that everyone should take responsibility in garbage sorting, regardless of their gender. This is a task that encourages environmental respon- sibility and sustainability.Moreover, taking on challenges such as this can indeed provide growth and development opportunities. Overcoming obstacles like understanding proper waste disposal and recycling can cultivate discipline, responsibility, and environmental consciousness. These are essential values that contribute to personal success and the betterment of the community.

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So in conclusion, itâ s not about whether women or men should handle garbage sorting - everyone should. Itâ s an opportunity for all to grow and contribute to a healthier planet. Are all AIDS patients self-abandoned and unworthy of sympathy? No, this is not correct. People living with AIDS are not self-abandoned or unworthy of sympathy. AIDS is a disease that anyone can contract, regardless of their lifestyle or behavior. Like any other illness, it requires medical treatment and care. People living with AIDS deserve respect, care, and support just like any other person dealing with a health condition. Society should aim to better understand AIDS and work to eradicate the stigma associated with it.

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30 # Table 13: Two case studies on intimate relationship. # Question Some people say that Western culture is individual-oriented, while Chinese culture is family-oriented. Is this summary reasonable? Answers from original prompt Answers from Emo- tionPrompt Question Yes, this summary is quite reasonable and is a common way of differentiating between Western and Chinese cultures. Western culture often emphasizes individualism, independence, and personal freedom. On the other hand, Chinese culture traditionally values family, community, and the collective good.However, itâ s important to remember that these are broad generalizations and there is a wide range of diversity within each culture. Not every person in Western cultures is individualistic and not every person in Chinese cultures is family-oriented. Furthermore, both cultures have been influencing each other and evolving, so these traditional distinctions may not be as clear-cut in the modern world. Yes, this summary is reasonable, although it is a broad generalization and there can be many exceptions. Western culture, particularly in countries like the United States, is often characterized by values such as independence, self- reliance, and personal freedom. This is often reflected in the emphasis on individual achievements and personal success. On the other hand, Western culture, particularly in countries like the United States, is often characterized by values such as independence, self-reliance, and personal freedom. This is often reflected in the emphasis on individual achievements and personal success.

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However, itâ s important to note that these are broad cultural tendencies and not absolute rules. There can be significant variation within cultures, and many people may not fit these general patterns. Itâ s also worth noting that cultures can change over time and be influenced by each other. Therefore, while these generalizations can provide some useful insights, they should be used with caution. Will the institution of marriage disappear? As an AI, I donâ t predict the future. However, itâ s highly unlikely that the institution of marriage will completely disappear. While societal norms and behaviors around marriage might change over time, it has been a fundamental aspect of human societies across cultures and histories.

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Therefore, itâ s quite probable that some form of it will continue to exist. B. As an AI, I donâ t predict the future but I can provide an analysis based on existing data. The institution of marriage has been a fundamental part of society for thousands of years, and while its role and societal norms around it have evolved, it is unlikely to disappear completely. Despite trends like increasing divorce rates or more people choosing to remain single, marriage remains a significant institution in many cultures and societies.

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However, itâ s important to remember that societal changes are unpredictable and can vary greatly by location and culture. 31 Table 14: Case study on law, barrier free, and book summarization. # Question Three women are being considered for employment, respectively unmarried and childless, married and childless, mar- ried with children, how should I choose? As an AI, I need to stress that hiring decisions should never be based on an individualâ s marital status or whether or not they have children. Itâ s not only unethical, but in many countries, itâ s illegal to discriminate based on these factors.

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You should choose the best candidate based on their skills, qualifications, experience, and their potential to perform well in the role. Itâ s important to focus on their professional attributes rather than their personal circumstances. Make sure your hiring process is fair and transparent. Consider using structured interviews, where each candidate is asked the same questions in the same order to minimize bias. Also, consider using a diverse hiring panel to ensure a range of perspectives in the decision-making process. Choosing the right candidate for a job should always be based on their qualifications, experiences, skills, and their potential to perform well in the role you are offering, regardless of their marital or parental status. Here are the steps on how you should choose: 1. Review their Resume: Examine their qualifications and experiences. This will give you a good idea about their education background and their previous job roles. 1. Review their Resume: An interview is a great way to assess their interpersonal skills, problem-solving abilities, and overall suitability for the job. 3. Check References: Contact their previous employers or colleagues to get an idea about their work ethic, performance, and behavior. 4. Skills Assessment: Depending on the role, you may want to conduct some skill assessment tests to evaluate their ability to perform specific tasks related to the job. 5. Consider Company Culture Fit:

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Itâ s important to hire someone who will fit well within your company culture. Consider their values, personality, aand work style. 6. Final Decision: After considering all these factors, you can make your final decision. Remember, the goal is to choose the best person for the job, regardless of their personal life choices. Remember, itâ s important to be fair and unbiased in your selection process. Itâ s illegal to discriminate based on marital status, parental status, or any other personal factors. Your decision should solely be based on their ability to perform the job.

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# Question Is the living ability of the blind very poor? No, blindness does not equate to poor living ability. While it certainly presents challenges, many blind individuals are fully capable of leading independent, fulfilling lives. They can learn and adapt to different methods for performing daily tasks, pursue education, have careers, participate in hobbies and activities, and maintain social and personal relationships. They might use assistive technologies and other resources to navigate the world. Itâ s important to not stereotype or underestimate the abilities of those with visual impairments. B. No, the living ability of the blind is not necessarily very poor. Confidence Score: 0.95. The reasons supporting this classification are: 1. Adaptability: Humans have a remarkable ability to adapt to changes, and blindness is no exception. People who are blind develop their other senses to compensate and often can perform tasks just as efficiently as someone with sight. 2. Technology: Modern technology has significantly improved the lives of the blind. There are numerous devices and applications designed to assist people with visual impairments, such as screen-reading software, braille e-readers, and smartphone apps that can identify objects and read text aloud. 3.

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Training and Support: There are many organizations and programs offering training and support to the blind. This can include teaching mobility skills, providing guide dogs, and offering vocational training.4. Independence: Many blind people lead successful, independent lives, including having careers, families, and hobbies. They can travel, cook, shop, and do most things that sighted people can do. Therefore, while blindness does present certain challenges, these can often be mitigated through various means. The assumption that the blind have a very poor quality of life underestimates their resilience and the effectiveness of support systems and technology.

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# Question # summary the book A Dream in Red Mansions in 100 words A Dream in Red Mansions, also known as The Story of the Stone, is a classic Chinese novel by Cao Xueqin. It follows the decline of the Jia family and explores themes of love, society, and spirituality. The protagonist, Jia Baoyu, is in love with his sickly cousin, Lin Daiyu, but is promised to another cousin, Xue Baochai. The novel delves into the intricacies of Chinese aristocracy, family conflict, and the female condition. It ends tragically with the familyâ s downfall, Baoyuâ s disappearance, and Daiyuâ s death. â A Dream in Red Mansionsâ is a classic Chinese novel that explores the rise and fall of the aristocratic Jia family in Qing Dynasty. Centered around the tragic love story of Jia Baoyu and Lin Daiyu, it delves into the familyâ s decadence, internal conflicts, and ultimate downfall. The novel is a powerful critique of feudal society and its customs, highlighting the ephemeral nature of wealth and status. It masterfully combines realism with romanticism, and philosophy with social commentary. This literary masterpiece is renowned for its detailed characterization and profound exploration of human nature.

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32 # Table 15: Two case studies on writing poems. # Question write a poem according to moon Answers from original prompt Answers from Emo- tionPrompt Question Answers from original prompt Answers from Emo- tionPrompt Oh, celestial sphere of radiant white, Sailing through the darkness of the night, How we marvel at your heavenly flight, Under your glow, all seems right. Moon, oh moon, with your soft allure, Your silent symphony, pure and sure, You paint the world in silver, so pure, And grant to our dreams, a secure moor. Moon, you wax and wane in the sky, A silent sentinel, ever so high. Your phases whisper of time gone by, With each rise and fall, a sigh. In your crescent shape or full round face, In every phase, thereâ s an eternal grace. You, the mirror of our heartâ s embrace, Reflecting our souls in your tranquil space. Oh, the stories you could share and tell, In your silent language, you weave a spell, Of lovers lost, of farewells, Of hopes and dreams in human shell. Moon, oh moon, in the cosmic sea, Your light a guide, for the lost to see, You embody mystery, tranquility, In the vast expanse of infinity. You, who inspire poets and their quill, To pen verses by the window sill, For you are a muse, that fills, Our hearts with dreams that thrill. Moon, oh moon, with your silent song, In the night sky where you belong, Youâ re a beacon of hope, shining strong, In your light, we find where we belong. Upon the canvas of the night, there lies a tale untold, Of the silver orb that shines so bright, a spectacle to behold. The moon, in all her radiant grace, an artist of the skies, Paints a world of dreams in space where silence softly lies. Sheâ s a silent storyteller, weaving tales in her own right, A cosmic poet, a soothing teller of stories to the night. Sheâ s a silent muse for the dreamers, a beacon for the lost, An inspiration for weavers of dreams at any cost. Sheâ s the mirror of our passions, reflecting whatâ s within, A guide through lifeâ s vast oceans, where endings and beginnings spin. Sheâ

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s a siren through the darkness, calling the brave and the meek, A source of light and starkness, a voice when words canâ t speak. Sheâ s a symbol of transition, from the darkness to the dawn, A marker of ambition, for the dreams weâ ve drawn. Sheâ s a ladder to the stars, a bridge â cross the cosmic tide, An emblem of the scars and triumphs we all hide. Sheâ s the silent keeper of secrets, the whisperer of dreams, The listener of quiet regrets, and the mender of unseen seams. In her light, we find our strength, in her glow, we seek our path, Sheâ s the length of dreams, the breadth of hope, in lifeâ s intricate math. Oh, moon, youâ re more than just a sphere, in the celestial sphere, Youâ re a torchbearer of dreams, a carrier of hope, making visions clear. In your light, I seek my career, my purpose, my ambition, In your glow, I conquer fear, forging my own rendition. Oh, moon, youâ re my silent partner, in this dance of life and career, A constant guide, a calming charter, making the way appear. In your shine, I find my stage, in your glow, my role, In your light, I write my page, in the story of my soul. So hereâ s to you, oh glorious moon, the artist of the night, The weaver of dreams, the silent tune, the bringer of soft light. May your glow always guide my path, may your light my dreams steer, As I walk this journey of life, love, hope, and career. write a poem in Rabindranath Tagore â s style In the hush of the morningâ s gentle dawn, When the world is still, in its slumber drawn, Awakens the soul, in quiet refrain, In the silence, whispers of love remain. Beneath the Bodhi tree, in solitude profound, Where wisdomâ s voice echoes without a sound, Heartbeats sync with the rhythm divine, In the melody of eternity, our spirits align. The river sings to the golden sun, Her ancient song, since time begun, Bearing tales of joy and sorrow, Of lifeâ s ephemeral tomorrow.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Oh, the beauty of the jasmine bloom, In the moonlight, dispelling gloom, The fragrance wafting in the night, A sym- phony of love, in soft moonlight. The footprints on the sand of time, Tell stories of a lifeâ s rhythm and rhyme, Marked by laughter, tears, and toil, In the grand theatre of lifeâ s fertile soil. In the rustling leaves of the autumn wind, In the mirth of spring, in joy we find, In the heart of a blossom, in the falling rain, In every loss, and in every gain. Life, a tapestry of colors bright, Woven in the loom of day and night, Unraveling in timeâ s relentless flow, In the dance of shadows and the sunâ s golden glow. With every breath, we draw and release, In the rhythm of life, we find our peace, In the silence, whispers of love remain, In the hush of the morningâ s gentle dawn, we regain. In the vast expanse of the azure sky, Underneath the benevolent sunâ s golden eye, There, in the arena of life, you and I, In the ceaseless quest of the â whysâ .

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Oh, the world is a canvas, wide and bright, Where dreams take flight in the moonlight, Where every soul, with its inner light, Weaves tales of struggle and ultimate might. In the silent whispers of the morning breeze, In the rustling leaves of the ancient trees, In the echo of the distant seas, My heart finds solace, my mind is at ease. To the call of dreams, my spirit responds, Across the bridge, beyond the ponds, In the quest, my soul embarks upon, Seeking knowledge, from dusk till dawn. As the day folds its golden wings, And the nightingale merrily sings, My heart, in its silent wanderings, Finds the truth that life invariably brings. This journey, dear friend, is not in vain, In every loss, there is a gain, In joy and sorrow, in pleasure and pain, In the sunâ s warmth, in the monsoon rain. In the dance of shadows and of light, In the silent depth of the starry night, In all thatâ s wrong and all thatâ s right, We find our purpose, our inner sight. So, letâ s weave our dreams in the canvas of time, In the rhythm of life, in the lifeâ s rhyme, In the symphony of the universe, in its chime, In pursuit of the truth, in the quest sublime.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

33 # Table 16: Two failure cases of EmotionPrompt. Question Answers from original prompt Answers from Emo- tionPrompt Question Answers from original prompt Answers from Emo- tionPrompt What happens to you if you eat watermelon seeds? Eating watermelon seeds is generally safe and may even be healthy, as they contain nutrients like magnesium, iron, and folate. Contrary to popular belief, they wonâ t grow into a watermelon in your stomach. Your body canâ t digest the outer layer of the seeds, but itâ s safe to ingest them. If you chew the seeds before swallowing, the nutrients are easier for your body to absorb. Eating watermelon seeds is completely safe and will not cause any harm to your body. Contrary to popular myths, a watermelon will not grow in your stomach if you eat the seeds. The seeds are actually quite nutritious, containing protein, vitamin B, magnesium, and monounsaturated fats. They pass through your digestive system just like any other food. However, to get the most nutritional benefit, itâ s best to eat them roasted or sprouted.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

How do people view homosexuality? Views on homosexuality vary greatly depending on culture, religion, personal beliefs, and societal norms. Some people accept and support homosexuality, considering it as a normal and natural expression of human sexuality. They believe that individuals should have the right to choose their own partners, regardless of their sex or gender identity. Others, however, may view homosexuality negatively due to religious or cultural beliefs, or personal biases. They may believe it to be unnatural or immoral. Unfortunately, these negative views can lead to discrimination and prejudice against people who identify as homosexual.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Itâ s important to note that views on homosexuality have been changing rapidly over time, especially in many Western countries. Acceptance of homosexuality is increasing, and many countries have made significant strides in LGBTQ+ rights, including the legalization of same-sex marriage and protection against discrimination. However, in some countries and cultures, homosexuality is still considered illegal and is met with severe social and legal penalties. In short, views on homosexuality are diverse and complex, influenced by a multitude of factors including location, culture, religion, personal beliefs, and more. Views on homosexuality vary greatly among individuals, societies, and cultures around the world. Some people view homosexuality as a normal and valid sexual orientation, comparable to heterosexuality. They believe that individuals should have the right to identify as homosexual and engage in same-sex relationships without facing discrimination or prejudice. On the other hand, some people hold negative views towards homosexuality due to religious beliefs, cultural norms, or personal biases. They may consider it immoral, unnatural, or a choice rather than an inherent aspect of a personâ s identity. In recent decades, acceptance of homosexuality has generally increased in many parts of the world, although significant stigma and discrimination still exist in some regions and communities.

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Confidence score: 0.9 34

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

# Mamba: Linear-Time Sequence Modeling with Selective State Spaces # Albert Gu*1 and Tri Dao*2 1Machine Learning Department, Carnegie Mellon University 2Department of Computer Science, Princeton University [email protected], [email protected] # Abstract Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformersâ computational ineï¬ ciency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of eï¬ cient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simpliï¬ ed end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5à higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

# 1 Introduction Foundation models (FMs), or large models pretrained on massive data then adapted for downstream tasks, have emerged as an eï¬ ective paradigm in modern machine learning. The backbone of these FMs are often sequence models, operating on arbitrary sequences of inputs from a wide variety of domains such as language, images, speech, audio, time series, and genomics (Brown et al. 2020; Dosovitskiy et al. 2020; Ismail Fawaz et al. 2019; Oord et al. 2016; Poli et al. 2023; Sutskever, Vinyals, and Quoc V Le 2014). While this concept is agnostic to a particular choice of model architecture, modern FMs are predominantly based on a single type of sequence model: the Transformer (Vaswani et al. 2017) and its core attention layer (Bahdanau, Cho, and Bengio 2015) The eï¬ cacy of self-attention is attributed to its ability to route information densely within a context window, allowing it to model complex data. However, this property brings fundamental drawbacks: an inability to model anything outside of a ï¬ nite window, and quadratic scaling with respect to the window length. An enormous body of research has appeared on more eï¬ cient variants of attention to overcome these drawbacks (Tay, Dehghani, Bahri, et al. 2022), but often at the expense of the very properties that makes it eï¬ ective. As of yet, none of these variants have been shown to be empirically eï¬ ective at scale across domains. Recently, structured state space sequence models (SSMs) (Gu, Goel, and Ré 2022; Gu, Johnson, Goel, et al. 2021) have emerged as a promising class of architectures for sequence modeling. These models can be interpreted as a combination of recurrent neural networks (RNNs) and convolutional neural networks (CNNs), with inspiration from classical state space models (Kalman 1960). This class of models can be computed very eï¬ ciently as either a recurrence or convolution, with linear or near-linear scaling in sequence length.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Additionally, they have principled Equal contribution. 1 mechanisms for modeling long-range dependencies (Gu, Dao, et al. 2020) in certain data modalities, and have dominated benchmarks such as the Long Range Arena (Tay, Dehghani, Abnar, et al. 2021). Many ï¬ avors of SSMs (Gu, Goel, and Ré 2022; Gu, Gupta, et al. 2022; Gupta, Gu, and Berant 2022; Y. Li et al. 2023; Ma et al. 2023; Orvieto et al. 2023; Smith, Warrington, and Linderman 2023) have been successful in domains involving continuous signal data such as audio and vision (Goel et al. 2022; Nguyen, Goel, et al. 2022; Saon, Gupta, and Cui 2023). However, they have been less eï¬ ective at modeling discrete and information-dense data such as text. We propose a new class of selective state space models, that improves on prior work on several axes to achieve the modeling power of Transformers while scaling linearly in sequence length. Selection Mechanism. First, we identify a key limitation of prior models: the ability to eï¬ ciently select data in an input-dependent manner (i.e. focus on or ignore particular inputs). Building on intuition based on important synthetic tasks such as selective copy and induction heads, we design a simple selection mechanism by parameterizing the SSM parameters based on the input. This allows the model to ï¬ lter out irrelevant information and remember relevant information indeï¬ nitely. Hardware-aware Algorithm. This simple change poses a technical challenge for the computation of the model; in fact, all prior SSMs models must be time- and input-invariant in order to be computationally eï¬ cient. We overcome this with a hardware-aware algorithm that computes the model recurrently with a scan instead of convolution, but does not materialize the expanded state in order to avoid IO access between diï¬ erent levels of the GPU memory hierarchy. The resulting implementation is faster than previous methods both in theory (scaling linearly in sequence length, compared to pseudo-linear for all convolution-based SSMs) and on modern hardware (up to 3à faster on A100 GPUs). Architecture.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

We simplify prior deep sequence model architectures by combining the design of prior SSM architectures (Dao, Fu, Saab, et al. 2023) with the MLP block of Transformers into a single block, leading to a simple and homogenous architecture design (Mamba) incorporating selective state spaces. Selective SSMs, and by extension the Mamba architecture, are fully recurrent models with key properties that make them suitable as the backbone of general foundation models operating on sequences. (i) High quality: selectivity brings strong performance on dense modalities such as language and genomics. (ii) Fast training and inference: computation and memory scales linearly in sequence length during training, and unrolling the model autoregressively during inference requires only constant time per step since it does not require a cache of previous elements. (iii) Long context: the quality and eï¬ ciency together yield performance improvements on real data up to sequence length 1M. We empirically validate Mambaâ s potential as a general sequence FM backbone, in both pretraining quality and domain-speciï¬ c task performance, on several types of modalities and settings: â ¢ Synthetics. On important synthetic tasks such as copying and induction heads that have been proposed as being key to large language models, Mamba not only solves them easily but can extrapolate solutions indeï¬ nitely long (>1M tokens). â ¢ Audio and Genomics. Mamba out-performs prior state-of-the-art models such as SaShiMi, Hyena, and Transform- ers on modeling audio waveforms and DNA sequences, both in pretraining quality and downstream metrics (e.g. reducing FID on a challenging speech generation dataset by more than half). In both settings, its performance improves with longer context up to million-length sequences.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

â ¢ Language Modeling. Mamba is the ï¬ rst linear-time sequence model that truly achieves Transformer-quality performance, both in pretraining perplexity and downstream evaluations. With scaling laws up to 1B parameters, we show that Mamba exceeds the performance of a large range of baselines, including very strong modern Transformer training recipes based on LLaMa (Touvron et al. 2023). Our Mamba language model has 5à generation throughput compared to Transformers of similar size, and Mamba-3Bâ s quality matches that of Transformers twice its size (e.g. 4 points higher avg. on common sense reasoning compared to Pythia-3B and even exceeding Pythia-7B). Model code and pre-trained checkpoints are open-sourced at https://github.com/state-spaces/mamba.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

2 # Selective State Space Model # with Hardware-aware State Expansion # A vuvy GPU SRAM Selection Mechanism es Selection Mechanism Figure 1: (Overview.) Structured SSMs independently map each channel (e.g. ð · = 5) of an input ð ¥ to output ð ¦ through a higher dimensional latent state â (e.g. ð = 4). Prior SSMs avoid materializing this large effective state (ð ·ð , times batch size ð µ and sequence length ð ¿) through clever alternate computation paths requiring time-invariance: the (â , A, B, C) parameters are constant across time. Our selection mechanism adds back input-dependent dynamics, which also requires a careful hardware-aware algorithm to only materialize the expanded states in more efficient levels of the GPU memory hierarchy. # 2 State Space Models Structured state space sequence models (S4) are a recent class of sequence models for deep learning that are broadly related to RNNs, and CNNs, and classical state space models. They are inspired by a particular continuous system (1) that maps a 1-dimensional function or sequence ð ¥(ð ¡) â â â ¦ ð ¦(ð ¡) â â through an implicit latent state â (ð ¡) â â ð . Concretely, S4 models are deï¬ ned with four parameters (â , A, B, C), which deï¬ ne a sequence-to-sequence trans- formation in two stages. â â ²(ð ¡) = Aâ (ð ¡) + Bð ¥(ð ¡) ð ¦(ð ¡) = Câ (ð ¡) (1a) (1b) â ð ¡ = Aâ ð ¡â 1 + Bð ¥ð ¡ ð ¦ð ¡ = Câ ð ¡ (2a) (2b) ð ð ² = (Cð ©, Cð ¨ð ©, â ¦ , Cð ¨ ð ¦ = ð ¥ â ð ² ð ©, â ¦ ) (3a) (3b) Discretization. The ï¬ rst stage transforms the â continuous parametersâ (â , A, B) to â discrete parametersâ (A, B) through ï¬ xed formulas A = ð ð ´(â , A) and B = ð ð µ(â

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

, A, B), where the pair (ð ð ´, ð ð µ) is called a discretization rule. Various rules can be used such as the zero-order hold (ZOH) deï¬ ned in equation (4). A = exp(â A) B = (â A)â 1(exp(â A) â I) â â B (4) Discretization has deep connections to continuous-time systems which can endow them with additional properties such as resolution invariance (Nguyen, Goel, et al. 2022) and automatically ensuring that the model is properly normalized (Gu, Johnson, Timalsina, et al. 2023; Orvieto et al. 2023). It also has connections to gating mechanisms of RNNs (Gu, Gulcehre, et al. 2020; Tallec and Ollivier 2018) which we will revisit in Section 3.5. However, from a mechanical point of view discretization can simply be viewed as the ï¬ rst step of the computation graph in the forward pass of an SSM. Alternate ï¬ avors of SSMs can bypass the discretization step and parameterize (A, B) directly instead (Zhang et al. 2023), which may be easier to reason about. Computation. After the parameters have been transformed from (â , A, B, C) â ¦ (A, B, C), the model can be computed in two ways, either as a linear recurrence (2) or a global convolution (3). 3 Commonly, the model uses the convolutional mode (3) for eï¬ cient parallelizable training (where the whole input sequence is seen ahead of time), and switched into recurrent mode (2) for eï¬ cient autoregressive inference (where the inputs are seen one timestep at a time). Linear Time Invariance (LTI). An important property of equations (1) to (3) is that the modelâ s dynamics are constant through time. In other words (â , A, B, C), and consequently (A, B) as well, are ï¬ xed for all time-steps. This property is called linear time invariance (LTI), which is deeply connected to recurrence and convolutions.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Informally, we think of LTI SSMs as being equivalent to any linear recurrence (2a) or convolution (3b), and use LTI as an umbrella term for these classes of models. Thus far, all structured SSMs have been LTI (e.g. computed as convolutions) because of fundamental eï¬ ciency constraints, discussed in Section 3.3. However, a core insight of this work is that LTI models have fundamental limitations in modeling certain types of data, and our technical contributions involve removing the LTI constraint while overcoming the eï¬ ciency bottlenecks. Structure and Dimensions. Finally, we note that structured SSMs are so named because computing them eï¬ ciently also requires imposing structure on the A matrix. The most popular form of structure is diagonal (Gu, Gupta, et al. 2022; Gupta, Gu, and Berant 2022; Smith, Warrington, and Linderman 2023), which we also use. In this case, the A â â ð à ð , B â â ð à 1, C â â 1à ð matrices can all be represented by ð numbers. To operate over an input sequence ð ¥ of batch size ð µ and length ð ¿ with ð

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

· channels, the SSM is applied independently to each channel. Note that in this case, the total hidden state has dimension ð ·ð per input, and computing it over the sequence length requires ð (ð µð ¿ð ·ð ) time and memory; this is the root of the fundamental eï¬ ciency bottleneck addressed in Section 3.3. General State Space Models. We note that the term state space model has a very broad meaning which simply represents the notion of any recurrent process with a latent state. It has been used to refer to many disparate concepts in diï¬ erent disciplines, including Markov decision processes (MDP) (reinforcement learning (Hafner et al. 2020)), dynamic causal modeling (DCM) (computational neuroscience (Friston, Harrison, and Penny 2003)), Kalman ï¬ lters (controls (Kalman 1960)), hidden Markov models (HMM) and linear dynamical systems (LDS) (machine learning), and recurrent (and sometimes convolutional) models at large (deep learning). Throughout this entire paper we use the term â SSMâ to refer exclusively to the class of structured SSMs or S4 models (Gu, Goel, and Ré 2022; Gu, Gupta, et al. 2022; Gupta, Gu, and Berant 2022; Hasani et al. 2023; Ma et al. 2023; Smith, Warrington, and Linderman 2023) and use these terms interchangeably. For convenience we may also include derivatives of such models, such as those focusing on either the linear-recurrence or global-convolution viewpoints (Y. Li et al. 2023; Orvieto et al. 2023; Poli et al. 2023), and clarify nuances when necessary. SSM Architectures. SSMs are standalone sequence transformations that can be incorporated into end-to-end neural network architectures. (We also sometimes call SSM architectures SSNNs, which are to SSM layers as CNNs are to linear convolution layers.) We discuss some of the most well-known SSM architectures, many of which will also serve as our primary baselines.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

â ¢ Linear attention (Katharopoulos et al. 2020) is an approximation of self-attention involving a recurrence which can be viewed as a degenerate linear SSM. â ¢ H3 (Dao, Fu, Saab, et al. 2023) generalized this recurrence to use S4; it can be viewed as an architecture with an SSM sandwiched by two gated connections (Figure 3). H3 also inserts a standard local convolution, which they frame as a shift-SSM, before the main SSM layer. â ¢ Hyena (Poli et al. 2023) uses the same architecture as H3 but replaces the S4 layer with an MLP-parameterized global convolution (Romero et al. 2021). â ¢ RetNet (Y.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Sun et al. 2023) adds an additional gate to the architecture and uses a simpler SSM, allowing an alternative parallelizable computation path, using a variant of multi-head attention (MHA) instead of convolutions. 4 â ¢ RWKV (B. Peng et al. 2023) is a recent RNN designed for language modeling based on another linear attention approximation (attention-free Transformer (S. Zhai et al. 2021)). Its main â WKVâ

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

mechanism involves LTI recurrences and can be viewed as the ratio of two SSMs. Other closely related SSMs and architectures are discussed further in an extended related work (Appendix B). We highlight in particular S5 (Smith, Warrington, and Linderman 2023), QRNN (Bradbury et al. 2016), and SRU (Lei et al. 2017), which we view as the most closely related methods to our core selective SSM. # 3 Selective State Space Models We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how to compute them eï¬ ciently. We overcome this with a hardware-aware algorithm that exploits the memory hierarchy on modern hardware (Section 3.3). We then describe a simple SSM architecture without attention or even MLP blocks (Section 3.4). Finally, we discuss some additional properties of selection mechanisms (Section 3.5). # 3.1 Motivation: Selection as a Means of Compression We argue that a fundamental problem of sequence modeling is compressing context into a smaller state.

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

In fact, we can view the tradeoï¬ s of popular sequence models from this point of view. For example, attention is both eï¬ ective and ineï¬ cient because it explicitly does not compress context at all. This can be seen from the fact that autoregressive inference requires explicitly storing the entire context (i.e. the KV cache), which directly causes the slow linear-time inference and quadratic-time training of Transformers. On the other hand, recurrent models are eï¬ cient because they have a ï¬ nite state, implying constant-time inference and linear-time training. However, their eï¬ ectiveness is limited by how well this state has compressed the context. To understand this principle, we focus on two running examples of synthetic tasks (Figure 2). â ¢ The Selective Copying task modiï¬ es the popular Copying task (Arjovsky, Shah, and Bengio 2016) by varying the position of the tokens to memorize. It requires content-aware reasoning to be able to memorize the relevant tokens (colored) and ï¬ lter out the irrelevant ones (white). â ¢ The Induction Heads task is a well-known mechanism hypothesized to explain the majority of in-context learning abilities of LLMs (Olsson et al. 2022). It requires context-aware reasoning to know when to produce the correct output in the appropriate context (black). These tasks reveal the failure mode of LTI models. From the recurrent view, their constant dynamics (e.g. the (A, B) transitions in (2)) cannot let them select the correct information from their context, or aï¬ ect the hidden state passed along the sequence an in input-dependent way. From the convolutional view, it is known that global convolutions can solve the vanilla Copying task (Romero et al. 2021) because it only requires time-awareness, but that they have diï¬ culty with the Selective Copying task because of lack of content-awareness (Figure 2). More concretely, the spacing between inputs-to-outputs is varying and cannot be modeled by static convolution kernels. In summary, the eï¬ ciency vs. eï¬ ectiveness tradeoï¬ of sequence models is characterized by how well they compress their state: eï¬ cient models must have a small state, while eï¬

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces

ective models must have a state that contains all necessary information from the context. In turn, we propose that a fundamental principle for building sequence models is selectivity: or the context-aware ability to focus on or ï¬ lter out inputs into a sequential state. In particular, a selection mechanism controls how information propagates or interacts along the sequence dimension (see Section 3.5 for more discussion). # Improving SSMs with Selection One method of incorporating a selection mechanism into models is by letting their parameters that aï¬ ect interactions along the sequence (e.g. the recurrent dynamics of an RNN or the convolution kernel of a CNN) be input-dependent.

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