arXiv cs.CL· \"Omer Tekin Yavuz (\.Ileti\c{s}im Yaz{\i}l{\i}m)·· 10 小时前AI 评分31
因果神经元电路是否唯一且可迁移?研究揭示神经元组干预的冗余性与迁移边界
Are Causal Neuron Circuits Unique and Transferable?
AI 导读
针对 Qwen3-32B 中移除 18 个神经元即让"Scar"变"Simba"的经典演示,研究系统测量了 12 个 MLP 神经元的全部 4,096 个子集,发现完整神经元组很少是必要的:少数神经元往往就足够,且许多关系链上互不相交的子集各自都能单独翻转答案。
正文 · 原文
Abstract:Interpretability demonstrations often show that intervening on a group of neurons changes a model's answer. A recent example removes an 18-neuron group in Qwen3-32B and the model answers "Simba" instead of "Scar" to a two-hop Lion King riddle. Such a demonstration establishes that the group matters, but not how much of it is necessary, whether other groups could do the same, or whether the effect carries over to other inputs. We ask: when intervening on a group of neurons changes a model's answer, how much of that group is actually necessary? For every selected group we measure all 4,096 subsets of 12 MLP neurons. On a two-alternative capital task (Qwen3-0.6B), zero-ablating 12 selected neurons lowers the answer margin by 27% while random neurons barely change it, yet replacing the same neurons with their values from other questions also barely changes it: their activations are nearly constant across questions, and we find no evidence that they carry question-specific content. On two-hop relation chains, patching 12 neurons with activations from the first-hop question shifts the answer to the bridge entity (from Scar to Simba): the greedy answer starts with the bridge in 9/11 chains for Qwen3-1.7B, 9/11 for Llama-3.2-1B and 8/10 for Gemma-3-1B; random neurons never do. The full group is rarely necessary: a few neurons often suffice, and in many chains disjoint subsets each flip the answer on their own, in an exploratory analysis most often when the model is less confident. Group effects transfer to rephrasings of the same chain but are much weaker on other tested chains. We study neuron-level causal interventions and alternative subsets; we do not recover the full connectivity of circuits.
| Comments: | 12 pages, 5 figures, 4 tables. Code, data and results: this https URL |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.13885 [cs.LG] |
| (or arXiv:2611.13885v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13885 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ömer Tekin Yavuz [view email]
[v1]
Tue, 10 Nov 2026 10:47:06 UTC (701 KB)
来源:arXiv cs.CL · arxiv.org