[2604.07562] Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs

Computer Science > Computation and Language arXiv:2604.07562 (cs) [Submitted on 8 Apr 2026 (v1), last revised 20 Apr 2026 (this version, v2)] Title:Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs Authors:Tunazzina Islam View a PDF of the paper titled Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs, by Tunazzina Islam View PDF HTML (experimental) Abstract:Unsupervised methods are widely used to induce latent semantic structure from large text collections, yet their outputs often contain incoherent, redundant, or poorly grounded clusters that are difficult to validate without labeled data. We propose a reasoning-based refinement framework that leverages large language models (LLMs) not as embedding generators, but as semantic judges that validate and restructure the outputs of arbitrary unsupervised clustering algorithms. Our framework introduces three reasoning stages: (i) coherence verification, where LLMs assess whether cluster summaries are supported by their member texts; (ii) redundancy adjudication, where candidate clusters are merged or rejected based on semantic overlap; and (iii) label grounding, where clusters are assigned interpretable labels through a two-stage process that generates and consolidates semantically similar labels in a fully unsupervised manner. This design decouples representation learning from structural validation and mitigates the common failure modes of embedding-only approaches. We evaluate the framework ...