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arXiv Computation and Language By Rongyu Zhang, Mutian Bao, Jinjian Zhang, Yuetian Du, He Xu, Wei Zhou, Xiaojun Zhu, Ming Kong, Jie Liu, Linjian Mo, Qiang Zhu

Don't Guess, Ask Wisely: Benchmarking and Training Proactive Elicitation in Open-Domain LLM Conversations

Read the original on arXiv Computation and Language →

The paper introduces VagueBench, a benchmark that degrades seed queries into vague variants to evaluate large language models’ ability to ask follow‑up questions when user queries are incomplete. It proposes DialogGRPO, an offline reinforcement learning approach that models multi‑turn elicitation as a dialogue‑state directed acyclic graph, using dense turn‑level rewards to guide question selection. DialogGRPO achieves over 88% success in eliciting missing information on vague queries, improves answer coverage and recovery efficiency, and outperforms prompting and test‑time scaling baselines while generalizing zero‑shot to other dialogue datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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