LLME dge R efine: Enhancing Text Clustering with LLM -Based Boundary Point Refinement (2024)

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.

## introduction text clustering is a critical task in various nlp applications, such as topic modeling and information retrieval. effective clustering enables better data management and more insightful analysis. however, text clustering presents several challenges, particularly in handling edge points-data points that are difficult to assign to clusters due to their ambiguous or extreme characteristics. the advent of large language models (llms) offers new solutions to these challenges. llms possess powerful text understanding capabilities that can significantly improve clustering accuracy. for instance, idas @xcite integrates abstractive summarizations from llms directly into clustering processes, and clusterllm @xcite utilizes llm-predicted sentence relations to guide clustering. however, previous llm-enhanced clustering methods often require extensive llm api queries, lack domain generalization, or are not sufficiently effective. in this work, we focus on leveraging the text understanding and in-context learning capabilities of llms to handle the edge points that traditional methods struggle with. our proposed llmedgerefine text clustering method consists of a two-stage clustering edge points refinement processing. initially, we employ k-means to initialize clusters. in the first stage, we identify edge points using a hard threshold and then form super-points to perform efficient hierarchical secondary clustering. this approach enhances cluster quality by effectively mitigating the effects of outliers. the formation of super-points allows for a more granular examination of cluster boundaries, which is particularly beneficial for accurately delineating ambiguous data points. in the second stage, we leverage the advanced text understanding capabilities of llms to refine the cluster edges. this involves a soft edge points removal and re-assignment mechanism, where llms reassess and reassign edge points based on their semantic context. this step capitalizes on llms' ability to comprehend nuanced text relationships, thereby ensuring more accurate and reliable clustering results. we validate our method through extensive experiments on eight diverse datasets. the results demonstrate that our method consistently outperforms baseline approaches in terms of clustering accuracy. additionally, our complexity analysis confirms that our method is more efficient than state-of-the-art techniques, making it a practical choice for large-scale applications. in summary, our contributions are as follows: ## related work clustering, a cornerstone of unsupervised learning, has seen diverse applications across various data modalities, including text, images, and graphs @xcite @xcite @xcite @xcite . traditional approaches such as kmeans @xcite and agglomerative clustering (day and edelsbrunner, 1984) initially dominated, operating on vector representations to partition data based on similarity measures like euclidean distance or cosine similarity @xcite . recent years have witnessed a paradigm shift towards deep clustering, leveraging deep neural networks to enhance clustering. @xcite categorizes deep clustering into multi-stage @xcite , iterative @xcite @xcite , generative @xcite , and simultaneous methods @xcite . more recent research has also explored llmenhanced clustering. @xcite expands clustering applications to interpretability and explanation generation tasks. in unsupervised clustering, idas @xcite integrates abstractive summarizations from llms directly into clustering processes, highlighting the trend towards leveraging advanced nlp models for clustering tasks. a state-of-the-art method, clusterllm @xcite , utilizes llm-predicted sentence relations to guide clustering. however, clusterllm requires extensive llm queries and domain-specific fine-tuning, limiting efficiency and generalizability. semi-supervised approaches, such as @xcite , require a subset of ground truth labels or expert feedback, whereas our work focuses on unsupervised clustering. ## experimental setup datasets and baselines. in our experimental evaluation, we assess llmedgerefine across diverse datasets, including clinc(i), mtop(i), massive(i) @xcite , goemo @xcite , clinc-domain, mtop-domain, and massive-scenario. these datasets cover intent classification, topic modeling, emotional clustering, and domain-specific scenarios. we compare llmedgerefine against established unsupervised baselines including idas @xcite and clusterllm @xcite . the detailed statistics of these datasets is listed in ## conclusion in this work, we introduced llmedgerefine, a novel text clustering method enhanced by llms. our method effectively addresses the challenges posed by outlier data points and domain-specific fine-tuning requirements observed in traditional clustering approaches. the experimental results demonstrate not only the effectiveness but also the efficiency of llmedgerefine.