常识与道德：LLMs中叙述焦点偏见的奇特案例
Common Sense vs. Morality: The Curious Case of Narrative Focus Bias in LLMs
AI总结：
本文研究了LLMs在道德困境中优先道德推理而非常识理解的偏见，提出CoMoral数据集并揭示了模型在识别常识矛盾时的叙述焦点偏见问题。
摘要：
大型语言模型（LLMs）正在被越来越多地应用于多样化的现实世界应用和用户群体中。因此，这些模型必须既具有道德基础又具备知识意识。在本工作中，我们揭示了当前LLMs的一个关键限制——它们倾向于优先考虑道德推理而非常识理解。为了研究这一现象，我们引入了CoMoral，一个包含常识矛盾嵌入在道德困境中的新基准数据集。通过对十种不同大小的LLMs进行广泛评估，我们发现现有模型在没有先验信号的情况下始终无法识别此类矛盾。此外，我们观察到一种普遍的叙述焦点偏见，即LLMs在将常识矛盾归因于次要角色而非主要角色（叙述者）时更容易检测到矛盾。我们的全面分析强调了需要增强推理意识训练以提高大型语言模型的常识鲁棒性。
英文摘要：
Large Language Models (LLMs) are increasingly deployed across diverse real-world applications and user communities. As such, it is crucial that these models remain both morally grounded and knowledge-aware. In this work, we uncover a critical limitation of current LLMs -- their tendency to prioritize moral reasoning over commonsense understanding. To investigate this phenomenon, we introduce CoMoral, a novel benchmark dataset containing commonsense contradictions embedded within moral dilemmas. Through extensive evaluation of ten LLMs across different model sizes, we find that existing models consistently struggle to identify such contradictions without prior signal. Furthermore, we observe a pervasive narrative focus bias, wherein LLMs more readily detect commonsense contradictions when they are attributed to a secondary character rather than the primary (narrator) character. Our comprehensive analysis underscores the need for enhanced reasoning-aware training to improve the commonsense robustness of large language models.
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。
