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Noise-Aware Verbal Confidence CAlibration Rules are proposed to provide a principled foundation for resolving overconfidence under noise and NOVA, a noise-aware calibration framework that synthesizes supervision from ~2K HotpotQA examples guided by these rules.
Real-world decision-making often involves uncertainty expressed in linguistic rather than numerical terms, and Prospect Theory (PT) provides a classic framework for modeling human behavior under such uncertainty. Although recent studies have developed frameworks to estimate PT parameters for Large Language Models (LLMs...
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