ReLTEx：基于大语言模型的可靠分类体系扩展

中文摘要 AI 辅助

近期大语言模型（LLMs）的进展已展现出生成语义相关概念与关系的强大能力，使其成为分类体系扩充的有前景工具。然而，直接依赖LLM生成的扩展内容常产生含噪、冗余或层级不一致的结构，限制了其在自动分类体系扩展中的可靠性。本文提出ReLTEx，一种基于LLM的可靠分类体系扩展框架，该框架将LLM驱动的候选生成与结构感知验证、递归扩展控制相结合，通过减少幻觉提升生成分类体系的一致性与质量。我们在掩码分类体系扩展设置下使用基准分类体系评估该框架，并对比多种验证策略，经适配评估指标与人工评估支持的实验结果表明，ReLTEx能生成更可靠、语义更连贯的分类体系扩展。

英文摘要

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.

发表机构

• Télécom Paris（巴黎电信学院）
• Institut Polytechnique de Paris（巴黎综合理工学院）

机构由 AI 辅助整理，请以论文原文为准。