Diversity of Thought Improves Reasoning Abilities of Large Language Models
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2310.07088
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Published
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5
Prompt
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Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
Paper • 2310.04474 • Published • 2 -
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques
Paper • 2310.08101 • Published • 2 -
Instance Needs More Care: Rewriting Prompts for Instances Yields Better Zero-Shot Performance
Paper • 2310.02107 • Published • 3 -
AskIt: Unified Programming Interface for Programming with Large Language Models
Paper • 2308.15645 • Published • 2 -
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Paper • 2305.10601 • Published • 11 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 32 -
FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Paper • 2309.04663 • Published • 5 -
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Paper • 2309.13165 • Published • 1 -
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Paper • 2305.11186 • Published • 1 -
Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs
Paper • 2309.03118 • Published • 2 -
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Paper • 2308.09729 • Published • 5 -
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Paper • 2304.14856 • Published • 1 -
Prompt Engineering and Calibration for Zero-Shot Commonsense Reasoning
Paper • 2304.06962 • Published • 1 -
Efficient Prompting via Dynamic In-Context Learning
Paper • 2305.11170 • Published • 1 -
Adapting Language Models to Compress Contexts
Paper • 2305.14788 • Published • 1 -
Visual Prompt Tuning
Paper • 2203.12119 • Published • 1 -
Do We Really Need a Large Number of Visual Prompts?
Paper • 2305.17223 • Published • 1 -
Better Zero-Shot Reasoning with Role-Play Prompting
Paper • 2308.07702 • Published • 2 -
Scaled Prompt-Tuning for Few-Shot Natural Language Generation
Paper • 2309.06759 • Published • 1 -
Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting
Paper • 2310.05824 • Published • 1 -
Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
Paper • 2307.08303 • Published • 1 -
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker
Paper • 2305.13729 • Published • 1 -
Context Aware Query Rewriting for Text Rankers using LLM
Paper • 2308.16753 • Published • 1 -
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Paper • 2306.04735 • Published • 1 -
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding
Paper • 2306.04933 • Published • 1 -
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization
Paper • 2303.12314 • Published • 1 -
Contrastive Learning for Prompt-Based Few-Shot Language Learners
Paper • 2205.01308 • Published • 1 -
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
Paper • 2305.18169 • Published • 1 -
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
Paper • 2308.08285 • Published • 1 -
Privacy-Preserving Prompt Tuning for Large Language Model Services
Paper • 2305.06212 • Published • 1 -
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Paper • 2211.03044 • Published • 1 -
ConsPrompt: Easily Exploiting Contrastive Samples for Few-shot Prompt Learning
Paper • 2211.04118 • Published • 1 -
Contrastive Demonstration Tuning for Pre-trained Language Models
Paper • 2204.04392 • Published • 1 -
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
Paper • 2305.18507 • Published • 1 -
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Paper • 2306.06427 • Published • 2 -
Progressive-Hint Prompting Improves Reasoning in Large Language Models
Paper • 2304.09797 • Published • 1 -
Small Language Models Improve Giants by Rewriting Their Outputs
Paper • 2305.13514 • Published • 2 -
Introspective Tips: Large Language Model for In-Context Decision Making
Paper • 2305.11598 • Published • 1 -
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
Paper • 2211.12588 • Published • 3 -
Improving ChatGPT Prompt for Code Generation
Paper • 2305.08360 • Published • 1 -
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting
Paper • 2305.07004 • Published • 1 -
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Paper • 2310.10698 • Published • 1 -
Test-Case-Driven Programming Understanding in Large Language Models for Better Code Generation
Paper • 2309.16120 • Published • 1 -
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners
Paper • 2110.06274 • Published • 1 -
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Paper • 2104.07650 • Published • 2 -
Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
Paper • 2305.01711 • Published • 1 -
Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models
Paper • 2210.12360 • Published • 1 -
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Paper • 2309.05173 • Published • 1 -
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Paper • 2205.05638 • Published • 3 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 53 -
Automatic Prompt Optimization with "Gradient Descent" and Beam Search
Paper • 2305.03495 • Published • 1 -
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Paper • 2307.15780 • Published • 26 -
XPrompt: Exploring the Extreme of Prompt Tuning
Paper • 2210.04457 • Published • 1 -
GPT Understands, Too
Paper • 2103.10385 • Published • 8 -
Reasoning with Language Model Prompting: A Survey
Paper • 2212.09597 • Published • 2 -
Automatic Chain of Thought Prompting in Large Language Models
Paper • 2210.03493 • Published • 2 -
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
Paper • 2306.03799 • Published • 1 -
Large Language Models are Better Reasoners with Self-Verification
Paper • 2212.09561 • Published • 1 -
Repository-Level Prompt Generation for Large Language Models of Code
Paper • 2206.12839 • Published • 2 -
Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering
Paper • 2304.07840 • Published • 1 -
Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks
Paper • 2310.10508 • Published • 1 -
Beyond Words: A Mathematical Framework for Interpreting Large Language Models
Paper • 2311.03033 • Published • 1 -
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
Paper • 2311.04254 • Published • 13 -
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
Paper • 2305.03268 • Published • 2 -
Prompt Sketching for Large Language Models
Paper • 2311.04954 • Published • 2 -
Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineering
Paper • 2308.07411 • Published • 2 -
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning
Paper • 2211.13218 • Published • 1 -
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
Paper • 2308.10445 • Published • 1 -
Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
Paper • 2307.05300 • Published • 18 -
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations
Paper • 2305.13235 • Published • 1 -
Prompt Engineering a Prompt Engineer
Paper • 2311.05661 • Published • 20 -
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
Paper • 2208.08340 • Published • 1 -
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification
Paper • 2309.09276 • Published • 1 -
Approximated Prompt Tuning for Vision-Language Pre-trained Models
Paper • 2306.15706 • Published • 1 -
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Paper • 2304.09402 • Published • 2 -
Prompting with Pseudo-Code Instructions
Paper • 2305.11790 • Published • 2 -
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
Paper • 2310.02304 • Published • 1 -
Cognitive Architectures for Language Agents
Paper • 2309.02427 • Published • 8 -
Contrastive Chain-of-Thought Prompting
Paper • 2311.09277 • Published • 34 -
Flows: Building Blocks of Reasoning and Collaborating AI
Paper • 2308.01285 • Published • 2 -
The Impact of Positional Encoding on Length Generalization in Transformers
Paper • 2305.19466 • Published • 2 -
OpenPrompt: An Open-source Framework for Prompt-learning
Paper • 2111.01998 • Published • 1 -
Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources
Paper • 2311.09732 • Published • 1 -
Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Paper • 2312.16171 • Published • 34 -
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion
Paper • 2312.14327 • Published • 6 -
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
Paper • 2305.13655 • Published • 7 -
Mixture of Soft Prompts for Controllable Data Generation
Paper • 2303.01580 • Published • 1 -
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
Paper • 2305.18170 • Published • 2 -
Large Language Models Are Human-Level Prompt Engineers
Paper • 2211.01910 • Published • 1 -
Metacognitive Prompting Improves Understanding in Large Language Models
Paper • 2308.05342 • Published • 2 -
Graph Prompt Learning: A Comprehensive Survey and Beyond
Paper • 2311.16534 • Published