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How well can LLMs solve chess puzzles? (github.com)

submitted 7 months ago by [email protected] to c/[email protected]

22 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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[–] [email protected] 23 points 7 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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