ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning
This addresses the challenge of efficiently updating commonsense knowledge in LLMs for improved reasoning, though it is incremental as it builds on existing knowledge editing methods.
The authors tackled the problem of editing commonsense knowledge in large language models (LLMs) to correct inaccuracies and improve reasoning, and their ConceptEdit framework enhanced plausibility in generated knowledge and achieved stronger performance on question-answering benchmarks.
Knowledge Editing (KE) aims to adjust a Large Language Model's (LLM) internal representations and parameters to correct inaccuracies and improve output consistency without incurring the computational expense of re-training the entire model. However, editing commonsense knowledge still faces difficulties, including limited knowledge coverage in existing resources, the infeasibility of annotating labels for an overabundance of commonsense knowledge, and the strict knowledge formats of current editing methods. In this paper, we address these challenges by presenting ConceptEdit, a framework that integrates conceptualization and instantiation into the KE pipeline for LLMs to enhance their commonsense reasoning capabilities. ConceptEdit dynamically diagnoses implausible commonsense knowledge within an LLM using another verifier LLM and augments the source knowledge to be edited with conceptualization for stronger generalizability. Experimental results demonstrate that LLMs enhanced with ConceptEdit successfully generate commonsense knowledge with improved plausibility compared to other baselines and achieve stronger performance across multiple question answering benchmarks. Our data, code, and models are publicly available at https://github.com/HKUST-KnowComp/ConKE.