Papers with bidirectional refinement between
Co-Evolving LLMs and Embedding Models via Density-Guided Preference Optimization for Text Clustering (2025.emnlp-main)
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| Challenge: | Existing methods for text clustering use static pseudo-oracles, i.e., unidirectionally querying them for similarity assessment or data augmentation. |
| Approach: | They propose a training framework that enables bidirectional refinement between LLMs and embedding models by using task-aware prompts to guide the LLM in generating interpretations for the input texts. |
| Outcome: | Experiments on 14 benchmark datasets across 5 tasks demonstrate the effectiveness of the proposed training framework. |