VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation
Paper
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2411.13281
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Published
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17
WizardLM Team
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Ziyang
authored
a
paper
about 1 month ago
wenxcs
authored
a
paper
3 months ago
Post
10677
🔥 🔥🔥
Excited to announce WizardLM new Paper: Auto Evol-Instruct!
🐦 Twitter: https://x.com/WizardLM_AI/status/1812857977122202087
📃 Paper: https://arxiv.org/pdf/2406.00770
🤖 1. Fully AI-Powered Pipeline
Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.
📈2. Scaling Evol-Instruct with Arena Learning
With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.
Excited to announce WizardLM new Paper: Auto Evol-Instruct!
🐦 Twitter: https://x.com/WizardLM_AI/status/1812857977122202087
📃 Paper: https://arxiv.org/pdf/2406.00770
🤖 1. Fully AI-Powered Pipeline
Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.
📈2. Scaling Evol-Instruct with Arena Learning
With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.
- 1 reply
Ziyang
authored
4
papers
7 months ago
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
Paper
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2406.07476
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Published
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32
CodeHalu: Code Hallucinations in LLMs Driven by Execution-based Verification
Paper
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2405.00253
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Published
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models
Paper
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2405.00390
•
Published
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Paper
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2401.13298
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Published
WizardLM
updated
a
Space
8 months ago
nlpxucan
authored
2
papers
8 months ago
Ziyang
authored
a
paper
9 months ago
Post
39310
🔥🔥🔥 Introducing WizardLM-2!
📙Release Blog: https://wizardlm.github.io/WizardLM2
✅Model Weights: microsoft/wizardlm-661d403f71e6c8257dbd598a
🐦Twitter: https://twitter.com/WizardLM_AI/status/1779899325868589372
We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.
WizardLM-2 8x22B is our most advanced model, and the best opensource LLM in our internal evaluation on highly complex tasks. WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.
🤗 WizardLM 2 Capacities:
1. MT-Bench (Figure-1)
The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary works such as GPT-4-Trubo and Glaude-3. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.
2. Human Preferences Evaluation (Figure 2)
Through this human preferences evaluation, WizardLM-2's capabilities are very close to the cutting-edge proprietary models such as GPT-4-1106-preview, and significantly ahead of all the other open source models.
🔍Method Overview:
As the natural world's human-generated data becomes increasingly exhausted through LLM training, we believe that: the data carefully created by AI and the model step-by-step supervised by AI will be the sole path towards more powerful AI.
In the past one year, we built a fully AI powered synthetic training system. (As shown in the Figure 3).
📙Release Blog: https://wizardlm.github.io/WizardLM2
✅Model Weights: microsoft/wizardlm-661d403f71e6c8257dbd598a
🐦Twitter: https://twitter.com/WizardLM_AI/status/1779899325868589372
We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.
WizardLM-2 8x22B is our most advanced model, and the best opensource LLM in our internal evaluation on highly complex tasks. WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.
🤗 WizardLM 2 Capacities:
1. MT-Bench (Figure-1)
The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary works such as GPT-4-Trubo and Glaude-3. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.
2. Human Preferences Evaluation (Figure 2)
Through this human preferences evaluation, WizardLM-2's capabilities are very close to the cutting-edge proprietary models such as GPT-4-1106-preview, and significantly ahead of all the other open source models.
🔍Method Overview:
As the natural world's human-generated data becomes increasingly exhausted through LLM training, we believe that: the data carefully created by AI and the model step-by-step supervised by AI will be the sole path towards more powerful AI.
In the past one year, we built a fully AI powered synthetic training system. (As shown in the Figure 3).
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- 35 replies
Ziyang
authored
3
papers
9 months ago
Positional Artefacts Propagate Through Masked Language Model Embeddings
Paper
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2011.04393
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Published
•
1
Augmented Large Language Models with Parametric Knowledge Guiding
Paper
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2305.04757
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Published
•
2
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Paper
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2404.00399
•
Published
•
41
WizardLM
updated
2
datasets
10 months ago
WizardLM
updated
3
models
12 months ago