Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed that not all neurons are active across different datasets, and this sparsity correlates positively with the task-specific ability, leading to advancements in model pruning and training efficiency. Traditional fine-tuning methods engage all parameters of LLMs, which is computationally expensive and may not be necessary. In contrast, Parameter-Efficient Fine-Tuning (PEFT) approaches aim to minimize the number of trainable parameters, yet they still operate at a relatively macro scale (e.g., layer-level). We introduce Neuron-Level Fine-Tuning (NeFT), a novel approach that refines the granularity of parameter training down to the individual neuron, enabling more precise and computationally efficient model updates. The experimental results show that NeFT not only exceeded the performance of full-parameter fine-tuning and PEFT but also provided insights into the analysis of neurons.

大型语言模型 (LLMs) 由表现出各种行为和角色的神经元构成，随着模型规模的扩大，它们变得越来越多样化。研究发现，并非所有神经元在不同的数据集上都活跃，这种稀疏性与任务特定能力呈正相关，为模型剪枝和训练效率的进展提供了基础。传统的微调方法涉及LLMs的所有参数，计算复杂且可能不必要。相反，参数高效的微调方法旨在减少可训练参数的数量，但它们仍在相对宏观（例如，层级）上操作。我们介绍了神经元级微调 (NeFT)，一种将参数训练的粒度细化到单个神经元，实现更精确、计算更高效的模型更新的新方法。实验结果表明，NeFT不仅超过了全参数微调和参数高效微调的性能，还为神经元分析提供了深入洞见。

专注于神经元：神经元级别的大语言模型有监督微调