# Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples

arXiv: https://arxiv.org/abs/2402.07386 · HTML: https://mlsanity.com/paper/2402.07386/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [Generated Knowledge Prompting for Commonsense Reasoning](https://mlsanity.com/paper/2110.08387/) (2021)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020)
- [Improving Hypernymy Detection with an Integrated Path-based and Distributional Method](https://mlsanity.com/paper/1603.06076/) (2016)
- [SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble](https://mlsanity.com/paper/1910.08192/) (2019)
- [Constructing Taxonomies from Pretrained Language Models](https://mlsanity.com/paper/2010.12813/) (2020)
- [OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding](https://mlsanity.com/paper/2105.07688/) (2021)
- [Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction](https://mlsanity.com/paper/2309.01715/) (2023)
- [End-to-End Reinforcement Learning for Automatic Taxonomy Induction](https://mlsanity.com/paper/1805.04044/) (2018)
- [Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction](https://mlsanity.com/paper/2202.04876/) (2022)
- [RaTE: a Reproducible automatic Taxonomy Evaluation by Filling the Gap](https://mlsanity.com/paper/2307.09706/) (2023)

## Built on by (most cited first, 24 of 24)

- [Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://mlsanity.com/paper/2404.15676/) (2024)
- [TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora](https://mlsanity.com/paper/2506.10737/) (2025)
- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [From Papers to Panoramas: Building Hierarchies of Scientific Literature at Scale](https://mlsanity.com/paper/2504.13834/) (2025)
- [Hierarchical Selective Classification](https://mlsanity.com/paper/2405.11533/) (2024)
- [Refining Wikidata Taxonomy using Large Language Models](https://mlsanity.com/paper/2409.04056/) (2024)
- [Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion](https://mlsanity.com/paper/2505.13282/) (2025)
- [Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims](https://mlsanity.com/paper/2506.10728/) (2025)
- [Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration](https://mlsanity.com/paper/2509.15786/) (2025)
- [Taxonomy Inference for Tabular Data Using Large Language Models](https://mlsanity.com/paper/2503.21810/) (2025)
- [Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification](https://mlsanity.com/paper/2602.11448/) (2026)
- [EvoTaxo: Building and Evolving Taxonomy from Social Media Streams](https://mlsanity.com/paper/2603.19711/) (2026)
- [Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?](https://mlsanity.com/paper/2606.22994/) (2026)
- [BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration](https://mlsanity.com/paper/2605.12520/) (2026)
- [Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings](https://mlsanity.com/paper/2605.21029/) (2026)
- [ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems](https://mlsanity.com/paper/2606.21597/) (2026)
- [CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph](https://mlsanity.com/paper/2606.30175/) (2026)
- [AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis](https://mlsanity.com/paper/2607.28618/) (2026)
- [An Agentic Hybrid Top-Down and Bottom-Up Approach to Knowledge Graph Generation](https://mlsanity.com/paper/2608.07023/) (2026)
- [Security and Privacy Taxonomy Generation from Mobile App Reviews](https://mlsanity.com/paper/2608.09049/) (2026)
- [ReLTEx: Reliable LLM-based Taxonomy Expansion](https://mlsanity.com/paper/2608.10970/) (2026)
- [EviMap: Evidence-Grounded Hierarchical Topic Maps for Exploring Unlabeled Corpora](https://mlsanity.com/paper/2609.06664/) (2026)
- [SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging](https://mlsanity.com/paper/2609.07307/) (2026)
- [TACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation](https://mlsanity.com/paper/2609.17956/) (2026)

## Abstract (arXiv)

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.