Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from
  Token and Parameter Levels
Authors: name: Junjie Ye, name: Yuming Yang, name: Yang Nan, name: Shuo Li, name: Qi Zhang, name: Tao Gui, name: Xuanjing Huang, name: Peng Wang, name: Zhongchao Shi, name: Jianping Fan

Large language models (LLMs) acquire substantial world knowledge during
pre-training, which is further shaped by post-training techniques such as
supervised fine-tuning (SFT). However, the impact of SFT on a model's knowledge
remains underexplored, limiting our ability to control knowledge change
behavior in fine-tuned models. To address this gap, we evaluate closed-book
question answering (CBQA) performance across five LLMs from the LLaMA-2 and
LLaMA-3 families. Surprisingly, models fine-tuned on 1,920 samples perform up
to 14% worse than those fine-tuned on only 240 samples. Furthermore, varying
the level of knowledge mastery in the fine-tuning data leads to performance
fluctuations of over 12%. To investigate these effects, we analyze model
behavior at both the token and parameter levels. Our analysis reveals that up
to 90% of parameter updates during SFT do not contribute to knowledge
enhancement. Restoring these updates can improve performance on the CBQA task,
depending on the characteristics of the fine-tuning data. These insights offer
practical guidance for developing fine-tuning strategies that more effectively
strengthen model knowledge.