EtCon: Edit-then-Consolidate for Reliable Knowledge Editing

TL;DR

EtCon introduces a two-stage knowledge editing framework for large language models that enhances editing reliability and preserves pre-trained capabilities, addressing real-world autoregressive generation challenges.

Contribution

The paper proposes EtCon, a novel edit-then-consolidate paradigm combining targeted fine-tuning and policy optimization for more reliable knowledge editing in LLMs.

Findings

Improves editing reliability in LLMs.

Better preserves pre-trained capabilities.

Enhances real-world generalization.

Abstract

Knowledge editing aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, achieving improved performance in controlled, teacher-forced evaluations. However, they still encounter challenges in real-world autoregressive generation scenarios, which greatly limit their practical applicability. Our empirical analysis reveals two issues: (1) Most methods degrade pre-trained capabilities after injecting new knowledge; (2) They may exhibit a discrepancy between stored parametric knowledge and inference-time autoregressive generation behavior. To this end, we propose EtCon, an edit-then-consolidate paradigm that couples targeted edits with post-edit consolidation. Specifically, our framework comprises two stages: (1) Targeted Proximal Supervised Fine-Tuning (TPSFT) performs a constrained targeted edit to update parametric knowledge while controlling policy drift. (2) Group Relative Policy Optimization (GRPO) consolidates the edit by aligning autoregressive trajectories with the intended fact. Extensive experiments demonstrate that our EtCon improves editing reliability and real-world generalization, while better preserving pre-trained capabilities.