CompCQR: Compositional Query Generation for Training-Free Conversational Search
Multi-turn interactions with LLMs are becoming increasingly common in information-seeking scenarios. However, user queries are often ambiguous and context-dependent, making them ill-suited for direct use as retriever queries. Conversational query reformulation (CQR) addresses this issue by rewriting the current utterance into a stand-alone query grounded in the dialogue history. Recent LLM-based CQR approaches achieve strong performance; however, their repeated LLM invocations and misalignment with downstream retrievers remain challenges. In this work, we begin from the observation that retrievers are highly sensitive to content ordering: simply reordering the same content can lead to cha...
| Authors | Yunah Jang, Kang-il Lee, Joongbo Shin et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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