LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement
Zijin Feng, Luyang Lin, Lingzhi Wang, Hong Cheng, Kam-Fai Wong
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.
BibTeX
@inproceedings{feng-etal-2024-llmedgerefine,
title = "{LLME}dge{R}efine: Enhancing Text Clustering with {LLM}-Based Boundary Point Refinement",
author = "Feng, Zijin and
Lin, Luyang and
Wang, Lingzhi and
Cheng, Hong and
Wong, Kam-Fai",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1025/",
doi = "10.18653/v1/2024.emnlp-main.1025",
pages = "18455--18462"
}