@inproceedings{zhong-etal-2025-react,
title = "{REACT}: Representation Extraction And Controllable Tuning to Overcome Overfitting in {LLM} Knowledge Editing",
author = "Zhong, Haitian and
Liu, Yuhuan and
Xu, Ziyang and
Liu, Guofan and
Liu, Qiang and
Wu, Shu and
Zhao, Zhe and
Wang, Liang and
Tan, Tieniu",
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.860/",
pages = "16994--17011",
ISBN = "979-8-89176-332-6",
abstract = "Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it{'}s contextually inappropriate. To address this challenge, we introduce REACT (Representation Extraction And Controllable Tuning), a unified two-phase framework designed for precise and controllable knowledge editing. In the initial phase, we utilize tailored stimuli to extract latent factual representations and apply Principal Component Analysis with a simple learnbale linear transformation to compute a directional ``belief shift'' vector for each instance. In the second phase, we apply controllable perturbations to hidden states using the obtained vector with a magnitude scalar, gated by a pre-trained classifier that permits edits only when contextually necessary. Relevant experiments on EVOKE benchmarks demonstrate that REACT significantly reduces overfitting across nearly all evaluation metrics, and experiments on COUNTERFACT and MQuAKE shows that our method preserves balanced basic editing performance (reliability, locality, and generality) under diverse editing scenarios."
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<abstract>Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. To address this challenge, we introduce REACT (Representation Extraction And Controllable Tuning), a unified two-phase framework designed for precise and controllable knowledge editing. In the initial phase, we utilize tailored stimuli to extract latent factual representations and apply Principal Component Analysis with a simple learnbale linear transformation to compute a directional “belief shift” vector for each instance. In the second phase, we apply controllable perturbations to hidden states using the obtained vector with a magnitude scalar, gated by a pre-trained classifier that permits edits only when contextually necessary. Relevant experiments on EVOKE benchmarks demonstrate that REACT significantly reduces overfitting across nearly all evaluation metrics, and experiments on COUNTERFACT and MQuAKE shows that our method preserves balanced basic editing performance (reliability, locality, and generality) under diverse editing scenarios.</abstract>
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%0 Conference Proceedings
%T REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing
%A Zhong, Haitian
%A Liu, Yuhuan
%A Xu, Ziyang
%A Liu, Guofan
%A Liu, Qiang
%A Wu, Shu
%A Zhao, Zhe
%A Wang, Liang
%A Tan, Tieniu
%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 zhong-etal-2025-react
%X Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. To address this challenge, we introduce REACT (Representation Extraction And Controllable Tuning), a unified two-phase framework designed for precise and controllable knowledge editing. In the initial phase, we utilize tailored stimuli to extract latent factual representations and apply Principal Component Analysis with a simple learnbale linear transformation to compute a directional “belief shift” vector for each instance. In the second phase, we apply controllable perturbations to hidden states using the obtained vector with a magnitude scalar, gated by a pre-trained classifier that permits edits only when contextually necessary. Relevant experiments on EVOKE benchmarks demonstrate that REACT significantly reduces overfitting across nearly all evaluation metrics, and experiments on COUNTERFACT and MQuAKE shows that our method preserves balanced basic editing performance (reliability, locality, and generality) under diverse editing scenarios.
%U https://aclanthology.org/2025.emnlp-main.860/
%P 16994-17011
Markdown (Informal)
[REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing](https://aclanthology.org/2025.emnlp-main.860/) (Zhong et al., EMNLP 2025)
ACL
- Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, and Tieniu Tan. 2025. REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 16994–17011, Suzhou, China. Association for Computational Linguistics.