this post was submitted on 25 Mar 2024
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32
How well can LLMs solve chess puzzles? (github.com)
submitted 10 months ago by [email protected] to c/[email protected]
21 comments fedilink hide all child comments
 

Each LLM is given the same 1000 chess puzzles to solve. See puzzles.csv. Benchmarked on Mar 25, 2024.

Model Solved Solved % Illegal Moves Illegal Moves % Adjusted Elo
gpt-4-turbo-preview 229 22.9% 163 16.3% 1144
gpt-4 195 19.5% 183 18.3% 1047
claude-3-opus-20240229 72 7.2% 464 46.4% 521
claude-3-haiku-20240307 38 3.8% 590 59.0% 363
claude-3-sonnet-20240229 23 2.3% 663 66.3% 286
gpt-3.5-turbo 23 2.3% 683 68.3% 269
claude-instant-1.2 10 1.0% 707 66.3% 245
mistral-large-latest 4 0.4% 813 81.3% 149
mixtral-8x7b 9 0.9% 832 83.2% 136
gemini-1.5-pro-latest* FAIL - - - -

Published by the CEO of Kagi!

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[โ€“] [email protected] 23 points 10 months ago (4 children)

People thinking LLMs should be even serviceable at chess didn't understand LLMs. They really aren't problem solving applications. They're optimized for making responses to questions that look like what a response should look like, not for being accurate. That's really clear if you ask them for mathematical proofs. They will generate proofs that look like the right sort of thing, but they won't be correct unless they have the specific proof in their training data.

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