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New research identifies 'weakening neurons' with outsize influence in LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a specific type of neuron in large language models called "weakening neurons" that play a significant role in the models' output. These neurons, primarily found in later layers of transformer models, exhibit a negative cosine similarity between their input and output weight vectors. Despite their relative scarcity, these weakening neurons activate frequently and exert a substantial influence on the model's behavior, particularly when gate values are negative. AI

IMPACT This research offers a new method for analyzing LLM internals, potentially leading to better model interpretability and control.

RANK_REASON The cluster contains a research paper detailing findings about the internal workings of transformer models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research identifies 'weakening neurons' with outsize influence in LLMs

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The cluster contains a research paper detailing findings about the internal workings of transformer models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sebastian Gerstner, Hilal AlQuabeh, Kentaro Inui, Hinrich Sch\"utze ·

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

    arXiv:2609.18612v1 Announce Type: cross Abstract: We analyze the learned input-output behavior of GLU-based neurons in large language models (LLMs). We propose a simple analysis method: For each neuron, we compute the cosine similarities between its input (reading) and output (wr…

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