面向大型语言模型事实生成的鲁棒不确定性量化
Robust Uncertainty Quantification for Factual Generation of Large Language Models
AI总结：
本文提出了一种新颖的鲁棒不确定性量化方法，用于评估大型语言模型在生成多事实任务中的可靠性，通过构建陷阱问题集验证了其有效性，提升了对抗性提问下的模型鲁棒性。
摘要：
大型语言模型（LLM）技术的快速发展使其被整合到各种专业和日常生活中。然而，LLM幻觉问题持续存在，已成为关键限制，显著影响了AI生成内容的可靠性和可信度。这一挑战在科学界引起了广泛关注，促使大量研究致力于幻觉检测和缓解策略。当前的方法框架揭示了关键局限性：传统不确定性量化方法主要在传统问答范式中有效，但面对非常规或对抗性提问策略时表现出明显不足。这种性能差距引发了对需要鲁棒批判性思维能力的现实应用中LLM响应可靠性的重大担忧。本研究旨在通过在生成多事实任务中提出不确定性量化场景来填补这一差距。我们精心构建了一组包含假名的陷阱问题。基于此场景，我们创新性地提出了一种新颖且稳健的不确定性量化方法（RU）。一系列实验已进行以验证其有效性。结果表明，构建的陷阱问题集表现优异。此外，与四种不同模型的基线方法相比，我们提出的方法显示出显著性能，与表现最好的基线方法相比，ROCAUC值平均增加了0.1-0.2，为解决LLM幻觉问题提供了新的视角和方法。
英文摘要：
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 novel and robust uncertainty quantification method(RU). A series of experiments have been conducted to verify its effectiveness. The results show that the constructed set of trap questions performs excellently. Moreover, when compared with the baseline methods on four different models, our proposed method has demonstrated great performance, with an average increase of 0.1-0.2 in ROCAUC values compared to the best performing baseline method, providing new sights and methods for addressing the hallucination issue of LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
