arXiv：cs.AI· Zach Studdiford, Gary Lupyan·· 5 小时前AI 评分58
研究：人类与 LLM 的日常推理都大量依赖模式匹配
Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning
AI 导读
Zach Studdiford 与 Gary Lupyan 在 arXiv 论文中测试了 46 个 LLM 与两批人类参与者在日常情境常识推理上的表现，发现两者呈现趋同的推理模式。通过分析内容不变与内容敏感神经元的作用，作者发现是内容敏感机制在因果上负责模型与人类对齐，表明人类与 LLM 的日常因果推理都大量使用模式匹配。
正文
Abstract:When large language models (LLMs) fail to generalize or make content-sensitive errors in reasoning, it is often taken as evidence that LLMs are not truly reasoning, but rather performing a kind of pattern matching. The implication is that human behavior does not exhibit the same types of failures because human reasoning relies on principled and content-invariant world models. We test this assumption by first evaluating humans and LLMs on their ability to engage in common-sense reasoning about a variety of everyday situations. Our results reveal convergent patterns of reasoning across 46 LLMs and two cohorts of human participants. We then ask whether this behavioral convergence is due to LLMs having acquired content-invariant world models or a set of pattern-matching heuristics by characterizing the roles of content-invariant and content-sensitive model neurons in producing human-like responses. We find that while LLMs encode both content-invariant and content-sensitive representations, it is content-sensitive mechanisms which are causally responsible for aligning models with humans. Taken together, our results suggest that everyday causal reasoning in people and LLMs makes heavy use of pattern-matching.
| Comments: | 13 pages main text, 59 pages supplementary text |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.13607 [cs.AI] |
| (or arXiv:2606.13607v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.13607 arXiv-issued DOI via DataCite |
Submission history
From: Zach Studdiford [view email]
[v1]
Thu, 11 Jun 2026 17:23:10 UTC (5,525 KB)
[v2]
Sat, 13 Jun 2026 20:58:19 UTC (5,526 KB)
[v3]
Thu, 1 Oct 2026 02:06:49 UTC (3,870 KB)
[v4]
Mon, 5 Oct 2026 23:56:28 UTC (3,858 KB)
来源：arXiv：cs.AI · arxiv.org