AI RESEARCH
RCEM: Embedder Equipped with Query Rewriting Skill for Robust Conversational Search in Distributional Shift
arXiv CS.CL
•
ArXi:2606.01697v1 Announce Type: new Conversational search has become increasingly important in retrieval-augmented generation (RAG) systems, where users interact with AI assistants through multi-turn conversations containing context-dependent queries. We propose RCEM, a conversational dense retrieval model that distills the query reformulation capability of LLMs into the embedding model, enabling context-aware retrieval without explicit query rewriting during inference.