49 citations · 101 across the 24 of their papers we have counts for
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cs.AI2026
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
Lihong He, Lucian Popa, Marina Danilevsky
Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a…
cs.CL2026★ 1 cited
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
Sara Rosenthal, Yannis Katsis, Vraj Shah +3
We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augmented generation, a popular use of large language models. We release a benchmark of 666 t…