Year: 2026
Type: conference-paper
Abstract: Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering based on their deep semantic understanding.However, their direct application is fundamentally limited by a lack of stateful memory for iterative refinement and the difficulty of managing cluster granularity.As a result, existing methods often rely on complex pipelines with external modules, sacrificing a truly end-to-end approach.We introduce LLM-MemCluster, a novel framework that reconceptualizes clustering as a fully LLM-native task.It leverages a Dynamic Memory to instill state awareness and a Dual-Prompt Strategy to enable the model to reason about and determine the number of clusters.Evaluated on several benchmark datasets, our tuning-free framework significantly and consistently outperforms strong baselines.LLM-MemCluster presents an effective, interpretable, and truly end-to-end paradigm for LLM-based text clustering.
Institution: University of Illinois Chicago
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1453181110, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G2154383771, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Category I: Crossing the Divide Between Today's Practice and Tomorrow's Science, Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5367127336, G5464919783, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI
OpenAlex ID: W7166813346