Summary of Arena Learning: Build Data Flywheel For Llms Post-training Via Simulated Chatbot Arena, by Haipeng Luo et al.
Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena
by Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Qingwei Lin, Jianguang Lou, Shifeng Chen, Yansong Tang, Weizhu Chen
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract introduces Arena Learning, an innovative offline strategy to evaluate large language models (LLMs) using AI-driven annotations. This approach facilitates continuous improvement through supervised fine-tuning and reinforcement learning. The method consists of two key elements: WizardArena, a pipeline predicting Elo rankings based on offline test sets, and the data flywheel updating training data based on battle results and refined models. Results demonstrate significant performance enhancements across various metrics for the target model, WizardLM-. This fully automated training and evaluation pipeline enables continuous advancements in LLMs via post-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Arena Learning is a new way to test language models using AI-powered simulations instead of human-annotated battles. This makes it faster and cheaper to train better language models. The approach has two main parts: predicting how well different models would do in online battles, and updating the training data based on which models are weak or strong. By doing this, the model can learn from its own mistakes and improve over time. |
Keywords
* Artificial intelligence * Fine tuning * Reinforcement learning * Supervised
