arXiv cs.CL· Sebastian Gerstner, Leonor Veloso, Hinrich Sch\"utze·· 13 小时前AI 评分23
GLUScope：分析 Transformer 语言模型中 GLU 神经元的开源工具
GLUScope: A Tool for Analyzing GLU Neurons in Transformer Language Models
AI 导读
GLUScope 是面向可解释性研究的开源资源，用于分析 Transformer 语言模型中 SwiGLU 等 GLU 类激活函数的神经元。该工具针对 gate 与 in 激活符号组合形成的四种激活象限，为每个神经元展示对应文本示例及出现频率，可揭示其他工具遗漏的可解释行为。创建该资源的代码同步开放，研究者可自行添加模型、文本语料或神经元页面。
正文 · 原文
Abstract:We present **GLUScope**, an open-source resource for analyzing neurons in Transformer-based language models (LMs), intended for interpretability researchers. We focus on the now-standard *GLU-based activation functions* such as SwiGLU, used in virtually all recent open-weights LMs. This introduces a new challenge: understanding positive activations is not enough. Instead, both the *gate* and the *in* activation of a neuron can be positive or negative, leading to four different possible sign combinations (*activation quadrants*) that in some cases have quite different functionalities (e.g. promoting vs. suppressing a specific set of tokens). Accordingly, for any neuron, our tool shows text examples for each of the four sign combinations, and indicates how often each combination occurs. Our resource can lead to novel insights: in particular, thanks to analyzing all four activation quadrants, it can surface interpretable behavior that other tools would miss. We also release the code used to create the resource, thus enabling researchers to build on it by adding more models, text corpora, or neuron pages.
| Comments: | 6 pages for main body, 10 pages in total. 4 figures |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.23826 [cs.CL] |
| (or arXiv:2602.23826v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.23826 arXiv-issued DOI via DataCite |
Submission history
From: Sebastian Gerstner [view email]
[v1]
Fri, 27 Feb 2026 09:07:45 UTC (312 KB)
[v2]
Wed, 21 Oct 2026 09:18:58 UTC (506 KB)
来源：arXiv cs.CL · arxiv.org