否定文本对大型语言模型幻觉的影响
The Impact of Negated Text on Hallucination with Large Language Models
AI总结：
本文研究否定文本对大型语言模型幻觉检测的影响，提出三个研究问题，构建NegHalu数据集，实验发现LLMs难以有效检测否定文本中的幻觉，并揭示内部状态处理挑战。
摘要：
近年来，关于大型语言模型（LLMs）幻觉的研究在自然语言处理领域进展迅速。然而，否定文本对LLMs幻觉的影响在很大程度上仍未得到探索。在本文中，我们提出了三个重要但尚未解答的研究问题，并旨在解决这些问题。为得出答案，我们研究了LLMs能否识别由否定引起的上下文转变，并仍能可靠地区分与肯定情况相当的幻觉。我们还通过使用否定表达重构现有的幻觉检测数据集，设计了NegHalu数据集。我们的实验表明，LLMs难以有效检测否定文本中的幻觉，经常产生逻辑不一致或不忠实的判断。此外，我们在词元级别追踪LLMs处理否定输入时的内部状态，并揭示了减轻其意外影响的挑战。
英文摘要：
Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. However, the impact of negated text on hallucination with LLMs remains largely unexplored. In this paper, we set three important yet unanswered research questions and aim to address them. To derive the answers, we investigate whether LLMs can recognize contextual shifts caused by negation and still reliably distinguish hallucinations comparable to affirmative cases. We also design the NegHalu dataset by reconstructing existing hallucination detection datasets with negated expressions. Our experiments demonstrate that LLMs struggle to detect hallucinations in negated text effectively, often producing logically inconsistent or unfaithful judgments. Moreover, we trace the internal state of LLMs as they process negated inputs at the token level and reveal the challenges of mitigating their unintended effects.
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