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English(EN) Multi-Hop Knowledge Composition is Bound by Pretraining Exposure

研究发现，LLM因预训练限制而在多跳推理方面遇到困难

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文探讨了大语言模型（LLMs）在执行多跳推理方面的局限性。该研究题为“多跳知识组合受预训练暴露的限制”，证明了即使在单个事实已知且可检索的情况下，LLMs在复杂的推理任务上也遇到困难。研究人员发现，这种失败根源于预训练阶段，因为在训练期间接触过组合式上下文的模型在未见过的组合式问题上表现更好，而未接触过的模型则继续失败。 AI

影响 强调了LLM推理能力的根本性局限，表明预训练数据组合是复杂问题解决的关键。

排序理由 研究论文，详细说明了LLM在多跳推理方面的局限性。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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研究发现，LLM因预训练限制而在多跳推理方面遇到困难

本文如何被排名

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Tool
研究论文，详细说明了LLM在多跳推理方面的局限性。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, model release
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yannis Karmim, Luis Marti, Djam\'e Seddah, Valentin Barri\`ere ·

    多跳知识组合受预训练暴露限制

    arXiv:2606.09338v2 Announce Type: replace Abstract: Large Language Models fail at implicit multi-hop reasoning: a model answers "When was $X$ born?" and "Who is $Y$'s closest friend?" correctly but fails on "When was $Y$'s closest friend born?" in a single forward pass, even when…

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