# LITE: LLM-Impelled efficient Taxonomy Evaluation

arXiv: https://arxiv.org/abs/2504.01369 · HTML: https://mlsanity.com/paper/2504.01369/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations](https://mlsanity.com/paper/2203.13928/) (2022)
- [Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation](https://mlsanity.com/paper/2306.05783/) (2023)
- [Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators](https://mlsanity.com/paper/2310.07289/) (2023)
- [TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations](https://mlsanity.com/paper/2202.04887/) (2022)
- [StrucText-Eval: Evaluating Large Language Model's Reasoning Ability in Structure-Rich Text](https://mlsanity.com/paper/2406.10621/) (2024)
- [LLMs Can Patch Up Missing Relevance Judgments in Evaluation](https://mlsanity.com/paper/2405.04727/) (2024)
- [A compendium and evaluation of taxonomy quality attributes](https://mlsanity.com/paper/2403.00111/) (2024)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation](https://mlsanity.com/paper/2303.14480/) (2023)

## Built on by (most cited first, 2 of 2)

- [ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems](https://mlsanity.com/paper/2606.21597/) (2026)
- [ReLTEx: Reliable LLM-based Taxonomy Expansion](https://mlsanity.com/paper/2608.10970/) (2026)

## Abstract (arXiv)

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 .