RCEM: Embedder Equipped with Query Rewriting Skill for Robust Conversational Search in Distributional Shift
arXiv:2606.01697v1 Announce Type: new Abstract: 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. Unlike prior conversational dense retrieval approaches that learn direct conversation-to-document matching, RCEM
The increasing complexity of conversational AI and the growing demand for robust RAG systems necessitate more advanced retrieval methods to handle distributional shifts effectively.
This development improves the reliability and efficiency of AI assistants, enhancing user experience and broadening the applicability of RAG systems in critical domains.
AI models can now perform context-aware conversational search without explicit query rewriting during inference, leading to more seamless and powerful interactions.
- · AI assistant developers
- · RAG system providers
- · Businesses implementing conversational AI
- · End-users of AI assistants
- · Legacy keyword-based search systems
- · AI models requiring manual query reformulation
Improved performance and user satisfaction in conversational AI applications.
Accelerated adoption of sophisticated RAG systems across various industries, including customer service and knowledge management.
Further blurring of the line between human and AI interaction, making AI agents more indistinguishable from human experts in specific tasks.
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Read at arXiv cs.CL