发现大模型中少数关键神经元，仅调整它们就能大幅提升性能。
Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts
- 通过多任务激活分析，找出模型中始终活跃的稀疏神经元群。
- 移除这些关键神经元后模型性能崩溃，证明其核心作用。
- 仅微调关键神经元即可媲美全参数微调，适合资源受限场景。
大型语言模型具备强大的综合能力，但其内部工作机制仍不清晰。本文发现，在多种开放权重的Transformer模型中，存在一个极小的神经元子集，在跨任务推理过程中持续高度激活。通过跨任务激活强度探测，我们识别出这一极稀疏的神经元集合，其移除会导致模型行为彻底崩溃，称为关键神经元。分析表明，这些神经元是模型中稳定且内在的组成部分，主要在预训练阶段形成。与这些神经元相关的参数在训练过程中被紧密校准，其精确值对模型能力至关重要。基于此，我们提出一种仅更新关键神经元的监督微调方法，在修改极少参数的前提下，实现与全参数微调相当甚至更优的任务性能提升，同时更好保留其他能力维度的表现。
原文摘要 · Abstract (English)
Large language models (LLMs) display strong comprehensive abilities, yet the internal mechanisms that support these behaviors remain insufficiently understood. In this work, we show that across a wide range of open-weight Transformers, a subset of neurons remains consistently highly activated during inference across tasks of multiple capability dimensions. By probing along the cross-task activation strength, an extremely sparse subset is isolated, whose removal causes a collapse in model behavior, which we term keystone neurons. Our analysis reveals that keystone neurons are a stable and intrinsic neuron subset of the model that is largely established during pretraining. The parameters associated with these neurons are tightly calibrated during the training process, and their precise values are critical for the capabilities of the model. Building on these insights, we propose a supervised fine-tuning approach that updates only keystone neurons, achieving task gains comparable to or even better than full-parameter fine-tuning while better preserving performance in other capability dimensions, despite modifying a much smaller number of parameters.
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