[2604.03904] I-CALM: Incentivizing Confidence-Aware Abstention for LLM Hallucination Mitigation

Computer Science > Computation and Language arXiv:2604.03904 (cs) [Submitted on 5 Apr 2026] Title:I-CALM: Incentivizing Confidence-Aware Abstention for LLM Hallucination Mitigation Authors:Haotian Zong, Binze Li, Yufei Long, Sinyin Chang, Jialong Wu, Gillian K. Hadfield View a PDF of the paper titled I-CALM: Incentivizing Confidence-Aware Abstention for LLM Hallucination Mitigation, by Haotian Zong and 5 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) frequently produce confident but incorrect answers, partly because common binary scoring conventions reward answering over honestly expressing uncertainty. We study whether prompt-only interventions -- explicitly announcing reward schemes for answer-versus-abstain decisions plus humility-oriented normative principles -- can reduce hallucination risk without modifying the model. Our focus is epistemic abstention on factual questions with a verifiable answer, where current LLMs often fail to abstain despite being uncertain about their answers. We first assess self-reported verbal confidence as a usable uncertainty signal, showing stability under prompt paraphrasing and reasonable calibration against a token-probability baseline. We then study I-CALM, a prompt-based framework that (i) elicits verbal confidence, (ii) partially rewards abstention through explicit reward schemes, and (iii) adds lightweight normative principles emphasizing truthfulness, humility, and responsibility. Using GPT-5 mini ...