ReLTEx: Reliable LLM-based Taxonomy Expansion

2026-08-11

AI Summary

Direct application of large language models (LLMs) to taxonomy expansion often introduces noise, redundancy, and hierarchical inconsistencies, undermining the reliability of automated extension. To address this, this work proposes ReLTEx, a novel framework that synergistically integrates LLM-generated candidate concepts with a structure-aware validation mechanism and a recursive expansion control strategy. This design effectively mitigates model hallucinations while preserving semantic coherence and hierarchical consistency in the expanded taxonomies. Through a comprehensive evaluation protocol combining masked assessment, tailored metrics, and human validation, experiments demonstrate that ReLTEx significantly outperforms existing methods across multiple benchmark taxonomies, achieving substantial improvements in both reliability and semantic fidelity.

Abstract

Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment. However, directly relying on LLM-generated expansions often leads to noisy, redundant, or hierarchically inconsistent structures, limiting their reliability for automated taxonomy expansion. In this paper, we present ReLTEx, a framework for reliable LLM-based taxonomy expansion. ReLTEx combines LLM-driven candidate generation with structure-aware validation and recursive expansion control to improve the consistency and quality of generated taxonomies by reducing hallucinations. We evaluate the proposed framework using benchmark taxonomies under a masked taxonomy expansion setting and compare multiple validation strategies. Experimental results, supported by both adapted evaluation metrics and human evaluation, demonstrate that ReLTEx produces more reliable and semantically coherent taxonomy expansions.

Problem

• taxonomy expansion
• Large Language Models
• hierarchical inconsistency
• reliability
• hallucination

Innovation

• taxonomy expansion
• Large Language Models
• structure-aware validation
• recursive expansion control
• hallucination reduction