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Chess study reveals LLMs struggle with generalization, not memorization

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv uses chess to evaluate the generalization and memorization capabilities of large language models (LLMs). The study found that LLMs, including Claude Opus and Gemini, perform poorly on novel chess positions that require generalization rather than memorization. When faced with unfamiliar scenarios, models often revert to random play or make illegal moves, indicating a failure to reliably apply game rules. While newer models show improvement, progress significantly slows on tasks with sparse prior information, suggesting limitations in systematic generalization beyond model scale. AI

IMPACT Highlights limitations in LLM generalization, suggesting current models may not reliably apply rules in novel situations without memorized data.

RANK_REASON Research paper published on arXiv detailing LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Chess study reveals LLMs struggle with generalization, not memorization

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Research paper published on arXiv detailing LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Leonard S. Pleiss, Maximilian Schiffer, Robert K. von Weizsaecker ·

    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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