Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning

TL;DR

This paper introduces a token categorization and forgetting mechanism to improve large language model fine-tuning by focusing on useful information and explicitly forgetting misleading tokens.

Contribution

It proposes a novel token categorization and forgetting strategy to enhance fine-tuning effectiveness beyond traditional data quality reliance.

Findings

Forgetting mechanism improves performance across benchmarks

Token categorization helps models focus on useful information

Experimental results show consistent gains with the proposed method

Abstract

Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs), notably enhancing their capacity to acquire domain-specific knowledge while preserving or potentially augmenting their general-purpose capabilities. However, the efficacy of SFT hinges on data quality as well as data volume, otherwise it may result in limited performance gains or even degradation relative to the associated baselines. To mitigate such reliance, we suggest categorizing tokens within each corpus into two parts -- positive and negative tokens -- based on whether they are useful to improve model performance. Positive tokens can be trained in common ways, whereas negative tokens, which may lack essential semantics or be misleading, should be explicitly forgotten. Overall, the token categorization facilitates the model to learn less informative messages, and the forgetting guides the model on what information to learn more precisely. We conduct experiments across diverse and well-established benchmarks using various model architectures, demonstrating that this forgetting mechanism enhances model performance.