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 cl
Record details
Published: 12 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement,” evidence record 18797, https://ethics.ai/record/18797 (originally published by arXiv).
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