用大模型高效评估知识图谱质量，自动发现逻辑错误。
LITE: LLM-Impelled efficient Taxonomy Evaluation
- 分层评估架构，逐级检验分类结构合理性。
- 在多个复杂任务中准确识别语义错误与逻辑矛盾。
- 适合需要快速验证知识体系的科研与工程人员。
本文提出LITE，一种基于大语言模型的分类体系评估方法，旨在实现高效、灵活的分类质量评估。针对大规模分类体系评估中存在的效率低、公平性差、结果不一致等问题，LITE采用自顶向下的分层评估策略，将分类体系分解为可管理的子结构，并通过交叉验证和标准化输入格式保障结果可靠性。该方法引入惩罚机制应对极端情况，并结合贴近任务目标的评估指标，提供定量分析与定性洞察。实验表明，LITE在复杂评估任务中表现出高可靠性，能有效识别分类体系中的语义错误、逻辑矛盾与结构缺陷，并给出优化方向。代码已开源：https://github.com/Zhang-l-i-n/TAXONOMY_DETECT。
原文摘要 · Abstract (English)
This paper presents LITE, an LLM-based evaluation method designed for efficient and flexible assessment of taxonomy quality. To address challenges in large-scale taxonomy evaluation, such as efficiency, fairness, and consistency, LITE adopts a top-down hierarchical evaluation strategy, breaking down the taxonomy into manageable substructures and ensuring result reliability through cross-validation and standardized input formats. LITE also introduces a penalty mechanism to handle extreme cases and provides both quantitative performance analysis and qualitative insights by integrating evaluation metrics closely aligned with task objectives. Experimental results show that LITE demonstrates high reliability in complex evaluation tasks, effectively identifying semantic errors, logical contradictions, and structural flaws in taxonomies, while offering directions for improvement. Code is available at https://github.com/Zhang-l-i-n/TAXONOMY_DETECT .
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