English(EN) Informational Antilocality and the Locality Bias in LLMs

大型语言模型在语言学习中难以处理非局域依赖

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员探讨了基于Transformer的大型语言模型（LLMs）学习“k-反局域语言”的能力，这类语言的特点是任何k个连续符号之间缺乏互信息。涉及这些构造语言的实验表明，无论反局域性水平如何，LLMs实现的交叉熵损失相似。然而，模型在训练更反局域语言时表现出更慢的收敛速度，这表明非局域依赖带来了更大的学习挑战，影响了速度而非最终的成功。 AI

影响这项研究表明，LLMs可能需要架构或训练上的改进，才能有效地处理复杂的非局域语言结构。

排序理由该集群包含一篇详细介绍LLM能力研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Andrew McInnerney, Shane Storks, Steven Abney, Richard L. Lewis · 2026-08-31 04:00

信息反局域性和大型语言模型中的局域性偏见

arXiv:2608.27760v1 Announce Type: new Abstract: We consider the ability of transformer-based language models (LLMs) to learn what we call k-antilocal languages, i.e., languages that have no mutual information across any span of $k$ contiguous symbols. We construct such languages …

本文如何被排名

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信息反局域性和大型语言模型中的局域性偏见

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