The authors introduce the task of asking clarifying questions for open-domain information-seeking conversations, collect the Qulac dataset from TREC topics, and propose a retrieval framework that outperforms baselines with an oracle showing 170% P@1 gain.
Bruce Croft
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MirrorBench defines a reproducible benchmark combining lexical metrics (MATTR, Yule's K, HD-D) and LLM-judge metrics with calibration controls to measure human-likeness of user-proxy agents across four datasets.
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Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
The authors introduce the task of asking clarifying questions for open-domain information-seeking conversations, collect the Qulac dataset from TREC topics, and propose a retrieval framework that outperforms baselines with an oracle showing 170% P@1 gain.
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MirrorBench: A Benchmark to Evaluate Conversational User-Proxy Agents for Human-Likeness
MirrorBench defines a reproducible benchmark combining lexical metrics (MATTR, Yule's K, HD-D) and LLM-judge metrics with calibration controls to measure human-likeness of user-proxy agents across four datasets.