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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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[-] sudoreboot@slrpnk.net 11 points 3 months ago
[-] conciselyverbose@sh.itjust.works 8 points 3 months ago* (last edited 3 months ago)

I wonder how many of the ones they "solved" were just because they'd seen it discussed somewhere in the data set, considering the puzzles are apparently from a public resource.

[-] sudoreboot@slrpnk.net 8 points 3 months ago

Yeah, I don't know why anyone knowledgeable would expect them to be good at chess. LLMs don't generalise, reason or spot patterns, so unless they read a chess book where the problems came from...

[-] Carrolade@lemmy.world 5 points 3 months ago

Likely close to 100%. If you read the (rather good) article, a little further down they test whether the LLM can play an extremely simplistic "Connect 4" game they devise, as a way of narrowing down on specifically reasoning capabilities.

It cannot.

Chess puzzles, in particular, are frequently shared and discussed in online chess spaces, so the LLM will have a significant amount of material to work with when it tries to predict the best response to give to the prompt.

[-] ColeSloth@discuss.tchncs.de 1 points 3 months ago

I didn't figure. I'm sure they could be taught to be much better, but normal computing can already play chess more or less perfectly. There really isn't much any room left to be gained.

this post was submitted on 25 Mar 2024
32 points (75.8% liked)

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