mlsanityData to 14 Oct 2026

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

arXiv 2602.23826 ↗First posted 27 Feb 2026cs.CL cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 27 Feb 2026.

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17 of 40 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  4. 2020Transformer Feed-Forward Layers Are Key-Value Memoriescited by 1,336
  5. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 713
  6. 2024OLMo: Accelerating the Science of Language Modelscited by 613
  7. 2022Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Spacecited by 532
  8. 2024Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Researchcited by 483
  9. 2023Finding Neurons in a Haystack: Case Studies with Sparse Probingcited by 334
  10. 2018What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Modelscited by 124
  11. 2025Are Sparse Autoencoders Useful? A Case Study in Sparse Probingcited by 115
  12. 2024Universal Neurons in GPT2 Language Modelscited by 102
5 more
  1. 2018On the importance of single directions for generalizationcited by 99
  2. 2023Neurons in Large Language Models: Dead, N-gram, Positionalcited by 88
  3. 2025Sparse Autoencoders Do Not Find Canonical Units of Analysiscited by 88
  4. 2024Confidence Regulation Neurons in Language Modelscited by 57
  5. 2024What does the Knowledge Neuron Thesis Have to do with Knowledge?cited by 49

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