Text Clustering as Classification with LLMs

• URL: http://arxiv.org/abs/2410.00927v3
• Date: Tue, 07 Oct 2025 10:17:31 GMT
• Title: Text Clustering as Classification with LLMs
• Authors: Chen Huang, Guoxiu He,
• Abstract summary: We propose a novel framework that reframes text clustering as a classification task by harnessing the in-context learning capabilities of Large Language Models.<n>By leveraging the advanced natural language understanding and generalization capabilities of LLMs, the proposed approach enables effective clustering with minimal human intervention.<n> Experimental results on diverse datasets demonstrate that our framework achieves comparable or superior performance to state-of-the-art embedding-based clustering techniques.
• Score: 9.128151647718251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation [52.794544682493814]
  Large language models (LLMs) provide strong contextual reasoning, yet prior work mainly uses them as auxiliary modules to refine embeddings or adjust cluster boundaries.<n>We propose ClusterFusion, a hybrid framework that treats the LLM as the clustering core, guided by lightweight embedding methods.<n> Experiments on three public benchmarks and two new domain-specific datasets demonstrate that ClusterFusion achieves state-of-the-art performance on standard tasks.
  arXiv  Detail & Related papers  (2025-12-04T00:49:43Z)
• ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering [79.69917150582633]
  Multi-modal large language models (MLLMs) can be leveraged to achieve user-driven clustering.<n>Our method first discovers that MLLMs' hidden states of text tokens are strongly related to the corresponding features.<n>We also employ a lightweight clustering head augmented with pseudo-label learning, significantly enhancing clustering accuracy.
  arXiv  Detail & Related papers  (2025-11-30T04:36:51Z)
• LLM-MemCluster: Empowering Large Language Models with Dynamic Memory for Text Clustering [52.41664454251679]
  Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering.<n>Existing methods often rely on complex pipelines with external modules, sacrificing a truly end-to-end approach.<n>We introduce LLM-MemCluster, a novel framework that reconceptualizes clustering as a fully LLM-native task.
  arXiv  Detail & Related papers  (2025-11-19T13:22:08Z)
• In-Context Clustering with Large Language Models [50.25868718329313]
  ICC captures complex relationships among inputs through an attention mechanism.<n>We show that pretrained LLMs exhibit impressive zero-shot clustering capabilities on text-encoded numeric data.<n>Our work extends in-context learning to an unsupervised setting, showcasing the effectiveness and flexibility of LLMs for clustering.
  arXiv  Detail & Related papers  (2025-10-09T17:07:55Z)
• Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering [59.54662810933882]
  Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models, often lack coherence and granularity.<n>We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering.
  arXiv  Detail & Related papers  (2025-09-23T15:12:58Z)
• HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization [0.0]
  HERCULES is an algorithm and Python package designed for hierarchical k-means clustering of diverse data types.<n>It generates semantically rich titles and descriptions for clusters at each level of the hierarchy.<n>An interactive visualization tool facilitates thorough analysis and understanding of the clustering results.
  arXiv  Detail & Related papers  (2025-06-24T20:22:00Z)
• MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval [50.062817677022586]
  Zero-Shot Image Retrieval (ZS-CIR) methods typically train adapters that convert reference images into pseudo-text tokens.<n>We propose MLLM-Guided VLM Fine-Tuning with Joint Inference (MVFT-JI) to construct two complementary training tasks using only unlabeled images.
  arXiv  Detail & Related papers  (2025-05-26T08:56:59Z)
• Cost-Effective Text Clustering with Large Language Models [15.179854529085544]
  This paper proposes TECL, a cost-effective framework that taps into the feedback from large language models for accurate text clustering. Under the hood, TECL adopts our EdgeLLM or TriangleLLM to construct must-link/cannot-link constraints for text pairs. Our experiments on multiple benchmark datasets exhibit that TECL consistently and considerably outperforms existing solutions in unsupervised text clustering.
  arXiv  Detail & Related papers  (2025-04-22T06:57:49Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• Idiosyncrasies in Large Language Models [54.26923012617675]
  We unveil and study idiosyncrasies in Large Language Models (LLMs) We find that fine-tuning existing text embedding models on LLM-generated texts yields excellent classification accuracy. We leverage LLM as judges to generate detailed, open-ended descriptions of each model's idiosyncrasies.
  arXiv  Detail & Related papers  (2025-02-17T18:59:02Z)
• k-LLMmeans: Summaries as Centroids for Interpretable and Scalable LLM-Based Text Clustering [0.0]
  We introduce k-LLMmeans, a novel modification of the k-means clustering algorithm that utilizes LLMs to generate textual summaries as cluster centroids. This modification preserves the properties of k-means while offering greater interpretability. We present a case study showcasing the interpretability of evolving cluster centroids in sequential text streams.
  arXiv  Detail & Related papers  (2025-02-12T19:50:22Z)
• On Unsupervised Prompt Learning for Classification with Black-box Language Models [71.60563181678323]
  Large language models (LLMs) have achieved impressive success in text-formatted learning problems. LLMs can label datasets with even better quality than skilled human annotators. In this paper, we propose unsupervised prompt learning for classification with black-box LLMs.
  arXiv  Detail & Related papers  (2024-10-04T03:39:28Z)
• Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels [75.77877889764073]
  Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. This study explores whether solely utilizing unlabeled data can elicit strong model capabilities. We propose a new paradigm termed zero-to-strong generalization.
  arXiv  Detail & Related papers  (2024-09-19T02:59:44Z)
• ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models [13.850983885339206]
  We propose a simple yet effective method to contextualize a task toward a large language model (LLMs)<n>We show the effectiveness in text clustering tasks, empowering LLMs to perform text-to-text-based clustering and leading to improvements on several datasets.
  arXiv  Detail & Related papers  (2024-06-19T08:48:05Z)
• Adaptable and Reliable Text Classification using Large Language Models [7.962669028039958]
  This paper introduces an adaptable and reliable text classification paradigm, which leverages Large Language Models (LLMs) We evaluated the performance of several LLMs, machine learning algorithms, and neural network-based architectures on four diverse datasets. It is shown that the system's performance can be further enhanced through few-shot or fine-tuning strategies.
  arXiv  Detail & Related papers  (2024-05-17T04:05:05Z)
• Context-Aware Clustering using Large Language Models [20.971691166166547]
  We propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS) for efficient and effective supervised clustering of entity subsets. This paper introduces a novel approach towards clustering entity subsets using Large Language Models (LLMs) by capturing context via a scalable inter-entity attention mechanism.
  arXiv  Detail & Related papers  (2024-05-02T03:50:31Z)
• PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval [76.50690734636477]
  We propose PromptReps, which combines the advantages of both categories: no need for training and the ability to retrieve from the whole corpus. The retrieval system harnesses both dense text embedding and sparse bag-of-words representations.
  arXiv  Detail & Related papers  (2024-04-29T04:51:30Z)
• Text Clustering with LLM Embeddings [0.0]
  The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. Recent advancements in large language models (LLMs) have the potential to enhance this task. Findings indicate that LLM embeddings are superior at capturing subtleties in structured language.
  arXiv  Detail & Related papers  (2024-03-22T11:08:48Z)
• Zero-Shot Topic Classification of Column Headers: Leveraging LLMs for Metadata Enrichment [0.0]
  We propose a method to support metadata enrichment using topic annotations generated by three Large Language Models (LLMs): ChatGPT-3.5, GoogleBard, and GoogleGemini. We evaluate the impact of contextual information (i.e., dataset description) on the classification outcomes.
  arXiv  Detail & Related papers  (2024-03-01T10:01:36Z)
• Learning to Prompt with Text Only Supervision for Vision-Language Models [107.282881515667]
  One branch of methods adapts CLIP by learning prompts using visual information. An alternative approach resorts to training-free methods by generating class descriptions from large language models. We propose to combine the strengths of both streams by learning prompts using only text data.
  arXiv  Detail & Related papers  (2024-01-04T18:59:49Z)
• Generalized Category Discovery with Clustering Assignment Consistency [56.92546133591019]
  Generalized category discovery (GCD) is a recently proposed open-world task. We propose a co-training-based framework that encourages clustering consistency. Our method achieves state-of-the-art performance on three generic benchmarks and three fine-grained visual recognition datasets.
  arXiv  Detail & Related papers  (2023-10-30T00:32:47Z)
• Large Language Models Enable Few-Shot Clustering [88.06276828752553]
  We show that large language models can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering. We find incorporating LLMs in the first two stages can routinely provide significant improvements in cluster quality.
  arXiv  Detail & Related papers  (2023-07-02T09:17:11Z)
• Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations [24.3378487252621]
  We show that zero-shot text classification can be improved simply by clustering texts in the embedding spaces of pre-trained language models. Our approach achieves an average of 20% absolute improvement over prompt-based zero-shot learning.
  arXiv  Detail & Related papers  (2022-10-29T16:01:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.