LSHC: LLM-Guided Semantic Hierarchical Clustering for Imbalanced and Multi-Granular Text Data

Text clustering is a popular research topic that faces challenges such as multi-level granularity, cluster size imbalance, and local structure ambiguity in the embedding space. We propose LLM-Guided Semantic Hierarchical Clustering (LSHC), a bottom-up framework that combines embedding-based text clustering with semantic judgment from a large language model (LLM). LSHC constructs fine-grained initial clusters, which can preserve small semantic clusters. This makes the framework particularly suitable for imbalanced data. Then, an LLM is used to determine an appropriate optimization operation: MERGE, KEEP, or RECLUSTER. MERGE combines semantically similar micro-clusters, KEEP preserves distinct neighbor clusters, and RECLUSTER prevents error assignments from propagating to higher semantic levels by reorganizing micro-clusters with mixed semantics. Iteratively conducting these three operations constructs a semantic hierarchy and can be terminated according to the desired cluster granularity. Experiments on six benchmarks show that LSHC achieves notable improvements on several highly imbalanced datasets, particularly MASSIVE-Intent and MTOP-Intent.