Summary of Explore the Reasoning Capability Of Llms in the Chess Testbed, by Shu Wang et al.
Explore the Reasoning Capability of LLMs in the Chess Testbed
by Shu Wang, Lei Ji, Renxi Wang, Wenxiao Zhao, Haokun Liu, Yifan Hou, Ying Nian Wu
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed method improves the reasoning capabilities of large language models in playing chess by integrating annotated strategy and tactics. The approach involves finetuning a model with a dataset containing 1 million chess positions, each with candidate moves annotated by chess experts. This approach outperforms state-of-the-art commercial language models such as GPT, Claude, and Gemini in selecting better chess moves. The addition of language explanations enhances the reasoning capability of large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at playing chess! Researchers noticed that expert chess players use a mix of long-term strategies and short-term tactics to win games. They created a special dataset with 1 million chess positions, each with suggested moves from experts. By training a model on this data, they made it better at choosing good moves than other top models. Adding explanations for why the moves were chosen also helped the model make even better choices. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt
