LLMs能否想象超越二元困境的道德替代方案？
Can LLMs Imagine Moral Alternatives Beyond Binary Dilemmas?
AI总结：
针对LLMs在道德困境中缺乏替代方案想象能力的问题，提出MoralAltDataset数据集，通过引入妥协和重构替代方案，发现LLMs倾向于选择替代方案，且生成的替代方案在质量和伦理标准上优于人类。
摘要：
随着大型语言模型（LLMs）越来越多地被部署为道德顾问和代理，它们需要处理两种竞争价值之间的困境。然而，现有关于LLMs道德困境的研究忽视了人类道德认知的一个核心方面：想象超越给定选项的替代方案的能力。我们引入了MoralAltDataset，这是一个包含307个道德困境的数据集，涵盖叙事性顾问困境和面向AI的代理困境，每个困境都增加了妥协和重构的替代方案。我们首先检验当引入这些替代方案时，人类和LLMs是否会改变他们的判断。在15个LLMs中，我们发现妥协替代方案通常比任一原始选项更受青睐，从而显著重塑了道德选择。然后，我们使用成对偏好和基于专家的标准，评估LLM生成的替代方案与人类编写的替代方案的质量。结果表明，LLM生成的替代方案通常更受欢迎，并且更好地满足细粒度的结构和伦理标准，同时揭示了结构质量与实际可行性之间的权衡。
英文摘要：
As LLMs increasingly serve as moral advisors and agents, they must address conflicts between competing values. Yet prior work on moral dilemmas overlooks a central aspect of human moral cognition: imagining alternatives beyond the given options. We introduce MoralAltDataset, comprising 307 Advisor and AI-facing Agent dilemmas augmented with compromise and reframed alternatives. We compare human and LLM judgments in binary and four-option settings. Across human participants and 15 LLMs, aggregate moral choice distributions differ substantially between the two settings, with compromise often preferred over either original binary option. Results show value shifts and stronger human-LLM agreement on alternatives. Source-stratified results reveal a descriptive gap: human alternative-selection rates are similar across authoring sources, whereas LLMs select GPT-5-authored alternatives substantially more often. We then compare human-authored alternatives with outputs from three representative LLMs through pairwise preference and expert-based evaluations. Alternatives from these LLMs are generally preferred and better satisfy fine-grained structural and ethical criteria, while revealing a trade-off between structural quality and practical feasibility. Our dataset is available here: https://huggingface.co/datasets/jongchanch/MoralAltDataset, and our project page is here: https://jongchanchoi.com/moral-imagination
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。
