Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy
  Induction from Limited Examples
Qingkai Zeng Yuyang Bai Zhaoxuan Tan Shangbin Feng Zhenwen Liang Zhihan Zhang Meng Jiang
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.