# LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media

arXiv: https://arxiv.org/abs/2504.12325 · HTML: https://mlsanity.com/paper/2504.12325/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering](https://mlsanity.com/paper/1812.09551/) (2018)
- [Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling](https://mlsanity.com/paper/2403.16248/) (2024)
- [Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies](https://mlsanity.com/paper/2309.13063/) (2023)
- [False Information on Web and Social Media: A Survey](https://mlsanity.com/paper/1804.08559/) (2018)
- [AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators](https://mlsanity.com/paper/2402.11073/) (2024)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Constructing Taxonomies from Pretrained Language Models](https://mlsanity.com/paper/2010.12813/) (2020)
- [CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring](https://mlsanity.com/paper/2010.06714/) (2020)
- [Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction](https://mlsanity.com/paper/2309.01715/) (2023)

## Built on by (most cited first, 5 of 5)

- [CHisAgent: A Multi-Agent Framework for Event Taxonomy Construction in Ancient Chinese Cultural Systems](https://mlsanity.com/paper/2601.05520/) (2026)
- [EvoTaxo: Building and Evolving Taxonomy from Social Media Streams](https://mlsanity.com/paper/2603.19711/) (2026)
- [PROMPT2BOX:Improving LLM Weakness Discovery and Specificity Estimation by Uncovering Entailment Structure among Prompts](https://mlsanity.com/paper/2603.21438/) (2026)
- [SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models](https://mlsanity.com/paper/2605.00620/) (2026)
- [Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency](https://mlsanity.com/paper/2608.22266/) (2026)

## Abstract (arXiv)

With the rapid expansion of content on social media platforms, analyzing and comprehending online discourse has become increasingly complex. This paper introduces LLMTaxo, a novel framework leveraging large language models for the automated construction of taxonomies of factual claims from social media by generating topics at multiple levels of granularity. The resulting hierarchical structure significantly reduces redundancy and improves information accessibility. We also propose dedicated taxonomy evaluation metrics to enable comprehensive assessment. Evaluations conducted on three diverse datasets demonstrate LLMTaxo's effectiveness in producing clear, coherent, and comprehensive taxonomies. Among the evaluated models, GPT-4o mini consistently outperforms others across most metrics. The framework's flexibility and low reliance on manual intervention underscore its potential for broad applicability.