Related papers: Massive Activations in Large Language Models

Massive Activations in Large Language Models

URL: http://arxiv.org/abs/2402.17762v2
Date: Wed, 14 Aug 2024 16:00:49 GMT
Title: Massive Activations in Large Language Models
Authors: Mingjie Sun, Xinlei Chen, J. Zico Kolter, Zhuang Liu,
Abstract summary: We show the widespread existence of massive activations across various Large Language Models (LLMs) Massive activations lead to the concentration of attention probabilities to their corresponding tokens, and implicit bias terms in the self-attention output.
Score: 77.51561903918535
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We observe an empirical phenomenon in Large Language Models (LLMs) -- very few activations exhibit significantly larger values than others (e.g., 100,000 times larger). We call them massive activations. First, we demonstrate the widespread existence of massive activations across various LLMs and characterize their locations. Second, we find their values largely stay constant regardless of the input, and they function as indispensable bias terms in LLMs. Third, these massive activations lead to the concentration of attention probabilities to their corresponding tokens, and further, implicit bias terms in the self-attention output. Last, we also study massive activations in Vision Transformers. Code is available at https://github.com/locuslab/massive-activations.

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