English(EN) Disentangling generalization and memorization in large language models using chess

国际象棋研究揭示大型语言模型在泛化方面存在困难，而非记忆

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

一篇新发表在arXiv上的研究论文使用国际象棋来评估大型语言模型（LLMs）的泛化和记忆能力。研究发现，包括Claude Opus和Gemini在内的大型语言模型在需要泛化而非记忆的新颖国际象棋局面上表现不佳。当面对不熟悉的情境时，模型常常会退回到随机走子或做出非法走子，这表明它们无法可靠地应用游戏规则。尽管较新的模型有所改进，但在先验信息稀疏的任务上，进展显著放缓，这表明除了模型规模之外，系统性泛化能力存在局限性。 AI

影响凸显了大型语言模型泛化能力的局限性，表明当前模型在没有记忆数据的情况下，可能无法在新的情境中可靠地应用规则。

排序理由发表在arXiv上的研究论文，详细介绍了大型语言模型的能力。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Leonard S. Pleiss, Maximilian Schiffer, Robert K. von Weizsaecker · 2026-10-22 04:00

Disentangling generalization and memorization in large language models using chess

arXiv:2601.16823v3 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit remarkable capabilities, yet it remains unclear to what extent these reflect sophisticated recall or genuine reasoning ability. We introduce chess as a controlled testbed aimed at disentangli…

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Disentangling generalization and memorization in large language models using chess

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