Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
CoRR(2024)
摘要
Automatic taxonomy induction is crucial for web search, recommendation
systems, and question answering. Manual curation of taxonomies is expensive in
terms of human effort, making automatic taxonomy construction highly desirable.
In this work, we introduce Chain-of-Layer which is an in-context learning
framework designed to induct taxonomies from a given set of entities.
Chain-of-Layer breaks down the task into selecting relevant candidate entities
in each layer and gradually building the taxonomy from top to bottom. To
minimize errors, we introduce the Ensemble-based Ranking Filter to reduce the
hallucinated content generated at each iteration. Through extensive
experiments, we demonstrate that Chain-of-Layer achieves state-of-the-art
performance on four real-world benchmarks.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要