In-Context Editing: Learning Knowledge from Self-Induced Distributions
Authors: name: Siyuan Qi, name: Bangcheng Yang, name: Kailin Jiang, name: Xiaobo Wang, name: Jiaqi Li, name: Yifan Zhong, name: Yaodong Yang, name: Zilong Zheng
Organization: Beijing Institute for General Artificial Intelligence

The existing fine-tuning paradigm for language models is brittle in knowledge
editing scenarios, where the model must incorporate new information without
extensive retraining. This brittleness often results in overfitting, reduced
performance, and unnatural language generation. To address this, we propose
Consistent In-Context Editing (ICE), a novel approach that leverages the
model's in-context learning capability to tune toward a contextual distribution
rather than a one-hot target. ICE introduces a straightforward optimization
framework that includes both a target and a procedure, enhancing the robustness
and effectiveness of gradient-based tuning methods. We provide analytical
insights into ICE across four critical aspects of knowledge editing: accuracy,
locality, generalization, and linguistic quality, showing its advantages.
Experimental results across four datasets confirm the effectiveness of ICE and
demonstrate its potential for continual editing, ensuring that updated
information is incorporated while preserving the integrity of the model.