A compact 25M chess move predictor exceeds larger fine-tuned models on puzzles, indicating memorization in earlier claims, while LLM-Modulo raises general LLM move accuracy from 1.2% to 21.2% and validity to 95.3%.
Advances in Neural Information Processing Systems , volume=
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The paper compiles practical lessons on reproducible LM evaluation and introduces the lm-eval library to mitigate common methodological problems in NLP.
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Generalization or Memorization? Brittleness Testing for Chess-Trained Language Models
A compact 25M chess move predictor exceeds larger fine-tuned models on puzzles, indicating memorization in earlier claims, while LLM-Modulo raises general LLM move accuracy from 1.2% to 21.2% and validity to 95.3%.
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Lessons from the Trenches on Reproducible Evaluation of Language Models
The paper compiles practical lessons on reproducible LM evaluation and introduces the lm-eval library to mitigate common methodological problems in NLP.