Robust Uncertainty Quantification for Factual Generation of Large Language Models

📝 Original Info

• Title: Robust Uncertainty Quantification for Factual Generation of Large Language Models
• ArXiv ID: 2601.00348
• Date: 2026-01-01
• Authors: Yuhao Zhang, Zhongliang Yang, Linna Zhou

📝 Abstract

The rapid advancement of large language model (LLM) technology has facilitated its integration into various domains of professional and daily life. However, the persistent challenge of LLM hallucination has emerged as a critical limitation, significantly compromising the reliability and trustworthiness of AI-generated content. This challenge has garnered significant attention within the scientific community, prompting extensive research efforts in hallucination detection and mitigation strategies. Current methodological frameworks reveal a critical limitation: traditional uncertainty quantification approaches demonstrate effectiveness primarily within conventional question-answering paradigms, yet exhibit notable deficiencies when confronted with non-canonical or adversarial questioning strategies. This performance gap raises substantial concerns regarding the dependability of LLM responses in real-world applications requiring robust critical thinking capabilities. This study aims to fill this gap by proposing an uncertainty quantification scenario in the task of generating with multiple facts. We have meticulously constructed a set of trap questions contained with fake names. Based on this scenario, we innovatively propose a nove...

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