ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation

• URL: http://arxiv.org/abs/2512.04350v1
• Date: Thu, 04 Dec 2025 00:49:43 GMT
• Title: ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation
• Authors: Yiming Xu, Yuan Yuan, Vijay Viswanathan, Graham Neubig,
• Abstract summary: 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.
• Score: 52.794544682493814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text clustering is a fundamental task in natural language processing, yet traditional clustering algorithms with pre-trained embeddings often struggle in domain-specific contexts without costly fine-tuning. Large language models (LLMs) provide strong contextual reasoning, yet prior work mainly uses them as auxiliary modules to refine embeddings or adjust cluster boundaries. We propose ClusterFusion, a hybrid framework that instead treats the LLM as the clustering core, guided by lightweight embedding methods. The framework proceeds in three stages: embedding-guided subset partition, LLM-driven topic summarization, and LLM-based topic assignment. This design enables direct incorporation of domain knowledge and user preferences, fully leveraging the contextual adaptability of LLMs. Experiments on three public benchmarks and two new domain-specific datasets demonstrate that ClusterFusion not only achieves state-of-the-art performance on standard tasks but also delivers substantial gains in specialized domains. To support future work, we release our newly constructed dataset and results on all benchmarks.

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