2025
Co-Evolving LLMs and Embedding Models via Density-Guided Preference Optimization for Text Clustering
EMNLP 2025
Large language models (LLMs) have shown strong potential in enhancing text clustering when combined with traditional embedding models. However, existing methods predominantly treat LLMs as static pseudo-oracles, i.e., unidirectionally querying them for similarity assessment or data augmentation, whi