Xu, C., Yan, J., Yang, M., Fang, F., Chen, H., & Deng, C. (2026). Editing Is a Bargaining Game: Balanced Knowledge Editing in Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, 40(40), 34097–34105. https://doi.org/10.1609/aaai.v40i40.40704
Abstract:
Large Language Models (LLMs) are prone to generating incorrect or outdated information, thereby necessitating efficient and precise mechanisms for knowledge updates. Existing knowledge editing approaches, however, often encounter conflicts between two competing objectives: maintaining existing knowledge (preservation) and incorporating new information (editing). During gradient-based optimization, these conflicting objectives can lead to imbalanced update directions, where one gradient dominates, ultimately resulting in suboptimal learning dynamics. To address this challenge, we propose a balanced knowledge editing framework inspired by Nash bargaining theory. Our method guides the optimization process toward a Pareto stationary point, ensuring an equilibrium solution wherein any deviation from the final state would degrade the overall performance with respect to both objectives. This guarantees optimality in preserving prior knowledge while integrating new information. We empirically validate the effectiveness of our approach across a range of evaluation metrics on standard benchmark datasets. Extensive experiments show that our method consistently outperforms state-of-the-art techniques, achieving a superior balance between knowledge preservation and update accuracy.
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Funding Info:
This research / project is supported by the Ministry of Science and Technology - National Key R&D Program of China
Grant Reference no. : 2023YFC3305600
This research / project is supported by the Ministry of Education of China - Joint Fund of Ministry of Education of China
Grant Reference no. : 8091B022149, 8091B02072404
This research / project is supported by the National Natural Science Foundation of China - NA
Grant Reference no. : 62132016, 62571412