cs.CLFeb 27, 2026

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

Authors: Sebastian Gerstner, Hinrich Schütze

Organizations: LMU Munich and Munich Center for Machine Learning (MCML), Munich, Germany

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.

Figures & tables

Explore similar work

CardsList
  1. Weakening Neurons: An Input-Output Functionality in Transformers with Outsize Influence

    Sep 16, 2026Sebastian Gerstner, Hilal AlQuabeh, Kentaro Inui +1Transformer InterpretabilityTransformer FFNs

  2. InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

    Jun 9, 2025Yifan Luo, Zhennan Zhou, Bin DongRepresentation GeometryLLM Interpretability