Knowledge Editing through Chain-of-Thought

2024-12-23

arXiv.org

AI Summary

Existing context-aware knowledge editing methods are largely confined to specific tasks (e.g., multi-hop QA), rely heavily on structured triples and few-shot prompting, and suffer from poor generalizability and cross-task instability. To address these limitations, we propose EditCoT—the first tuning-free, chain-of-thought (CoT)-based framework for universal knowledge editing via iterative CoT generation and refinement. Its core innovation lies in eliminating strong assumptions about task types or knowledge formats: instead, it dynamically constructs and optimizes CoT reasoning paths to inject knowledge into arbitrary NLP tasks, enabling a unified, language- and task-agnostic editing paradigm. Evaluated on multilingual and multitask benchmarks, EditCoT achieves state-of-the-art performance, significantly improving editing stability, effectiveness, and generalization across diverse settings.

Technology Category

• Knowledge Representation and Reasoning: Action, Change, and Causality
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Large Language Models (LLMs) have demonstrated exceptional capabilities across a wide range of natural language processing (NLP) tasks. However, keeping these models up-to-date with evolving world knowledge remains a significant challenge due to the high costs of frequent retraining. To address this challenge, knowledge editing techniques have emerged to update LLMs with new information without rebuilding the model from scratch. Among these, the in-context editing paradigm stands out for its effectiveness in integrating new knowledge while preserving the model's original capabilities. Despite its potential, existing in-context knowledge editing methods are often task-specific, focusing primarily on multi-hop QA tasks using structured knowledge triples. Moreover, their reliance on few-shot prompting for task decomposition makes them unstable and less effective in generalizing across diverse tasks. In response to these limitations, we propose EditCoT, a novel knowledge editing framework that flexibly and efficiently updates LLMs across various tasks without retraining. EditCoT works by generating a chain-of-thought (CoT) for a given input and then iteratively refining this CoT process using a CoT editor based on updated knowledge. We evaluate EditCoT across a diverse range of benchmarks, covering multiple languages and tasks. The results demonstrate that our approach achieves state-of-the-art performance while offering superior generalization, effectiveness, and stability compared to existing methods, marking a significant advancement in the field of knowledge updating. Code and data are available at: https://github.com/bebr2/EditCoT.

Problem

• Updating LLMs with new knowledge without retraining
• Improving generalization across diverse tasks and languages
• Enhancing stability and effectiveness of in-context editing

Innovation

• Chain-of-Thought iterative refinement for knowledge editing
• Generalized framework across multiple languages and tasks
• No retraining required for efficient model updating