@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://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.emnlp-main.1025/",
    doi = "10.18653/v1/2024.emnlp-main.1025",
    pages = "18455--18462",
    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."
}