上下文編輯：從自我誘導的分佈中學習知識

In-Context Editing: Learning Knowledge from Self-Induced Distributions

June 17, 2024

作者: Siyuan Qi, Bangcheng Yang, Kailin Jiang, Xiaobo Wang, Jiaqi Li, Yifan Zhong, Yaodong Yang, Zilong Zheng

cs.AI

摘要

現有的語言模型微調範式在知識編輯情境下顯得脆弱，當模型需要納入新資訊而無需進行大量重新訓練時。這種脆弱性通常導致過度擬合、性能降低和不自然的語言生成。為了解決這個問題，我們提出了一種新方法，稱為一致性上下文編輯（ICE），利用模型的上下文學習能力來調整至上下文分佈，而非單一熱目標。ICE引入了一個直觀的優化框架，包括目標和程序，增強了基於梯度調整方法的韌性和效果。我們從知識編輯的四個關鍵方面：準確性、局部性、泛化性和語言質量，提供了ICE的分析洞察，展示其優勢。在四個數據集上的實驗結果證實了ICE的有效性，並展示了其持續編輯的潛力，確保更新的資訊被納入同時保持模型的完整性。

English

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.