ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs

• URL: http://arxiv.org/abs/2506.01386v2
• Date: Sat, 06 Sep 2025 00:54:52 GMT
• Title: ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs
• Authors: Manit Baser, Dinil Mon Divakaran, Mohan Gurusamy,
• Abstract summary: We present ThinkEval, a framework to quantify indirect knowledge leakage and ripple effects in model-editing.<n>ThinkEval builds and employs specialized knowledge graphs to analyze the causal structure of facts before and after editing.<n>We evaluate five editing techniques: AlphaEdit, RECT, ROME, MEMIT, and PRUNE.
• Score: 3.9295613363026174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust model-editing techniques are essential for deploying large language models (LLMs) in practical applications, to enable cost-effective ways to deal with challenges such as privacy breaches, bias mitigation and misinformation spread. For example, an LLM-based healthcare assistance may need to update out-dated or incorrect knowledge to prevent harmful recommendations. However, many editing techniques focus on isolated facts, which critically fail to prevent indirect knowledge leakage -- the unintended reconstruction of edited-out information through persistent causal links and contextual relationships. To assist users in selecting the right editing technique, we develop and present ThinkEval, a framework to systematically quantify indirect knowledge leakage and ripple effects in model-editing. ThinkEval builds and employs specialized knowledge graphs to analyze the causal structure of facts before and after editing. To support this approach, we present KnowGIC, a benchmark dataset comprising multi-step reasoning paths that precisely measure these complex knowledge transformation effects. We evaluate five editing techniques: AlphaEdit, RECT, ROME, MEMIT, and PRUNE across multiple LLMs. Our results show that these techniques struggle to balance indirect fact suppression with the preservation of related knowledge, compromising the contextual integrity of a model's knowledge. Our dataset is available at: https://anonymous.4open.science/r/KnowGIC.

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