Conversational Search
Overview
Conversational search combines information retrieval with multi-turn dialogue: systems rewrite context-dependent questions, retrieve from text or multimodal collections, and ask clarifying questions when intent or preferences are underspecified. Common approaches include sparse–dense hybrid retrieval and reranking, large language model query rewriting and retrieval-augmented generation, conversational embeddings, and interactive agents that elicit constraints or personalize results. Evaluation spans conversational retrieval benchmarks, product and video search, and newer tests of clarification, long-horizon tasks, robustness, and interaction cost. A central challenge is balancing retrieval accuracy and efficiency with asking at the right time, grounding answers in reliable sources, protecting private data, and avoiding overreliance on fluent but uncertain responses.
Latest papers
23 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.