Text clustering serves as a fundamental technique for organizing and interpreting unstructured textual data, particularly in contexts where manual annotation is prohibitively costly. With the rapid advancement of Large Language Models (LLMs) and their demonstrated effectiveness across a broad spectrum of NLP tasks, an emerging body of research has begun to explore their potential in the domain of text clustering. However, existing LLM-based approaches still rely on fine-tuned embedding models and sophisticated similarity metrics, rendering them computationally intensive and necessitating domain-specific adaptation. To address these limitations, we propose a novel framework that reframes text clustering as a classification task by harnessing the in-context learning capabilities of LLMs. Our framework eliminates the need for fine-tuning embedding models or intricate clustering algorithms. It comprises two key steps: first, the LLM is prompted to generate a set of candidate labels based on the dataset and then merges semantically similar labels; second, it assigns the most appropriate label to each text sample. By leveraging the advanced natural language understanding and generalization capabilities of LLMs, the proposed approach enables effective clustering with minimal human intervention. Experimental results on diverse datasets demonstrate that our framework achieves comparable or superior performance to state-of-the-art embedding-based clustering techniques, while significantly reducing computational complexity and resource requirements. These findings underscore the transformative potential of LLMs in simplifying and enhancing text clustering tasks. We make our code available to the public for utilization at https://github.com/ECNU-Text-Computing/Text-Clustering-via-LLM. We also provide the supplementary Appendix within the repository.

翻译：文本聚类是组织和解释非结构化文本数据的基础技术，尤其在人工标注成本过高的场景中具有重要价值。随着大语言模型的快速发展及其在广泛自然语言处理任务中展现出的卓越性能，新兴研究开始探索其在文本聚类领域的应用潜力。然而，现有基于大语言模型的方法仍需依赖微调的嵌入模型和复杂的相似性度量，导致计算密集且需要领域特定适配。为克服这些局限，本文提出一种创新框架，通过利用大语言模型的上下文学习能力，将文本聚类重构为分类任务。该框架无需微调嵌入模型或复杂聚类算法，包含两个关键步骤：首先提示大语言模型基于数据集生成候选标签集并合并语义相似标签；其次为每个文本样本分配最合适的标签。通过利用大语言模型先进的自然语言理解和泛化能力，所提方法能够以最小人工干预实现有效聚类。在多类数据集上的实验结果表明，该框架在达到与最先进基于嵌入的聚类技术相当或更优性能的同时，显著降低了计算复杂度和资源需求。这些发现彰显了大语言模型在简化和增强文本聚类任务方面的变革潜力。我们已在 https://github.com/ECNU-Text-Computing/Text-Clustering-via-LLM 公开代码以供使用，补充附录亦包含在代码库中。