ALEX:A Light Editing-knowledge Extractor

• URL: http://arxiv.org/abs/2511.14018v1
• Date: Tue, 18 Nov 2025 00:54:15 GMT
• Title: ALEX:A Light Editing-knowledge Extractor
• Abstract summary: This paper introduces ALEX, a lightweight knowledge editing framework.<n>The core innovation of ALEX is its hierarchical memory architecture, which organizes knowledge updates (edits) into semantic clusters.<n> Experiments on the MQUAKE benchmark demonstrate that ALEX significantly improves both the accuracy of multi-hop answers and the reliability of reasoning paths.
• Score: 4.356025557149574
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The static nature of knowledge within Large Language Models (LLMs) makes it difficult for them to adapt to evolving information, rendering knowledge editing a critical task. However, existing methods struggle with challenges of scalability and retrieval efficiency, particularly when handling complex, multi-hop questions that require multi-step reasoning. To address these challenges, this paper introduces ALEX (A Light Editing-knowledge Extractor), a lightweight knowledge editing framework. The core innovation of ALEX is its hierarchical memory architecture, which organizes knowledge updates (edits) into semantic clusters. This design fundamentally reduces retrieval complexity from a linear O(N) to a highly scalable O(K+N/C). Furthermore, the framework integrates an Inferential Query Synthesis (IQS) module to bridge the semantic gap between queries and facts , and a Dynamic Evidence Adjudication (DEA) engine that executes an efficient two-stage retrieval process. Experiments on the MQUAKE benchmark demonstrate that ALEX significantly improves both the accuracy of multi-hop answers (MultiHop-ACC) and the reliability of reasoning paths (HopWise-ACC). It also reduces the required search space by over 80% , presenting a promising path toward building scalable, efficient, and accurate knowledge editing systems.

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