LITE: LLM-Impelled efficient Taxonomy Evaluation
This addresses the challenge of efficient and flexible taxonomy evaluation for researchers and practitioners dealing with large-scale taxonomies, though it appears incremental as it builds on existing LLM-based approaches.
The paper tackles the problem of evaluating large-scale taxonomies by proposing LITE, an LLM-based method that uses a top-down hierarchical strategy with cross-validation and penalty mechanisms, achieving high reliability in identifying semantic errors, logical contradictions, and structural flaws.
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 .