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        <namePart type="given">Zijin</namePart>
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    <abstract>Text clustering is a fundamental task in natural language processing with numerous applications. However, traditional clustering methods often struggle with domain-specific fine-tuning and the presence of outliers. To address these challenges, we introduce LLMEdgeRefine, an iterative clustering method enhanced by large language models (LLMs), focusing on edge points refinement. LLMEdgeRefine enhances current clustering methods by creating super-points to mitigate outliers and iteratively refining clusters using LLMs for improved semantic coherence. Our method demonstrates superior performance across multiple datasets, outperforming state-of-the-art techniques, and offering robustness, adaptability, and cost-efficiency for diverse text clustering applications.</abstract>
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%0 Conference Proceedings
%T LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement
%A Feng, Zijin
%A Lin, Luyang
%A Wang, Lingzhi
%A Cheng, Hong
%A Wong, Kam-Fai
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F feng-etal-2024-llmedgerefine
%X Text clustering is a fundamental task in natural language processing with numerous applications. However, traditional clustering methods often struggle with domain-specific fine-tuning and the presence of outliers. To address these challenges, we introduce LLMEdgeRefine, an iterative clustering method enhanced by large language models (LLMs), focusing on edge points refinement. LLMEdgeRefine enhances current clustering methods by creating super-points to mitigate outliers and iteratively refining clusters using LLMs for improved semantic coherence. Our method demonstrates superior performance across multiple datasets, outperforming state-of-the-art techniques, and offering robustness, adaptability, and cost-efficiency for diverse text clustering applications.
%U https://aclanthology.org/2024.emnlp-main.1025
%P 18455-18462

Markdown (Informal)

[LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement](https://aclanthology.org/2024.emnlp-main.1025) (Feng et al., EMNLP 2024)

• LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement (Feng et al., EMNLP 2024)

ACL

• Zijin Feng, Luyang Lin, Lingzhi Wang, Hong Cheng, and Kam-Fai Wong. 2024. LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 18455–18462, Miami, Florida, USA. Association for Computational Linguistics.