Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model
This addresses the efficiency challenge for researchers and practitioners in AI by offering a more precise and computationally efficient fine-tuning approach, though it is incremental relative to existing PEFT methods.
The paper tackled the problem of computationally expensive fine-tuning in large language models by introducing Neuron-Level Fine-Tuning (NeFT), which refines parameter training to individual neurons, resulting in performance exceeding full-parameter fine-tuning and PEFT methods.
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.