@inproceedings{ye-etal-2025-analyzing,
title = "Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels",
author = "Ye, Junjie and
Yang, Yuming and
Nan, Yang and
Li, Shuo and
Zhang, Qi and
Gui, Tao and
Huang, Xuanjing and
Wang, Peng and
Shi, Zhongchao and
Fan, Jianping",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.25/",
pages = "471--513",
ISBN = "979-8-89176-332-6",
abstract = "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 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."
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<abstract>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 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.</abstract>
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%0 Conference Proceedings
%T Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
%A Ye, Junjie
%A Yang, Yuming
%A Nan, Yang
%A Li, Shuo
%A Zhang, Qi
%A Gui, Tao
%A Huang, Xuanjing
%A Wang, Peng
%A Shi, Zhongchao
%A Fan, Jianping
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ye-etal-2025-analyzing
%X 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 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.
%U https://aclanthology.org/2025.emnlp-main.25/
%P 471-513
Markdown (Informal)
[Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels](https://aclanthology.org/2025.emnlp-main.25/) (Ye et al., EMNLP 2025)
ACL
- Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, and Jianping Fan. 2025. Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 471–513, Suzhou, China. Association for Computational Linguistics.