CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement
In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete. In such cases, directly producing an answer often leads to overgeneralized, erroneous, or low-information responses. In contrast, asking clarifying questions can substantially improve interaction quality. However, existing approaches still rely heavily on manually annotated data or preference alignment to address two fundamental challenges: when clarification is necessary, and which aspect of the query should be clarified. This reliance incurs high annotation costs and limits generalization. To address these challenges, we propose CLAIM, an ...
| Authors | Kuangzhao Yang, Ziliang Zhao, Zhicheng Dou |
|---|---|
| Primary category | cs.AI |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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