English

GLUScope: A Tool for Analyzing GLU Neurons in Transformer Language Models

Computation and Language 2026-03-02 v1 Machine Learning

Abstract

We present GLUScope, an open-source tool for analyzing neurons in Transformer-based language models, intended for interpretability researchers. We focus on more recent models than previous tools do; specifically we consider gated activation functions such as SwiGLU. 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 that in some cases have quite different functionalities. Accordingly, for any neuron, our tool shows text examples for each of the four sign combinations, and indicates how often each combination occurs. We describe examples of how our tool can lead to novel insights. A demo is available at https: //sjgerstner.github.io/gluscope.

Cite

@article{arxiv.2602.23826,
  title  = {GLUScope: A Tool for Analyzing GLU Neurons in Transformer Language Models},
  author = {Sebastian Gerstner and Hinrich Schütze},
  journal= {arXiv preprint arXiv:2602.23826},
  year   = {2026}
}

Comments

6 pages for main body, 9 pages in total. 4 figures

R2 v1 2026-07-01T10:55:16.855Z