语言模型是否准备好应对艰难选择？
Are LLMs ready for HardChoices?
- University of Manchester（曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究通过新数据集检查大语言模型在重大社会问题上的立场，发现大小模型面对相关问题时很少中立、常不连贯，且在有立场的问题上有高度一致性。
AI中文摘要：
大量研究关注检查大语言模型（LLMs）是否存在政治偏见，主要聚焦于高级意识形态维度。本文通过新数据集“HardChoices”，检查LLMs在重大实质性社会问题上是否有稳健立场。结果显示，面对此类问题，大小LLMs很少宣称中立，常不连贯，且在有立场的问题上有高度一致性。
英文摘要：
A lot of research attention has been devoted to checking whether large language models (LLMs) are politically biased. This work has largely focused on high-level ideological dimensions, such as left--right or progressive--conservative, and it has been shown that while LLMs are predominantly left and progressive leaning, largely mimicking the biases in the training data, they can be to some extent steered to change their preferences in post-training. In this short note, we check if LLMs have robust stances with regard to major substantive societal issues, on which members of the same ideological camp are often in disagreement, summarised in a novel dataset \textsc{HardChoices}. We show that, faced with this line of questioning, LLMs, both large and small, surprisingly rarely declare neutrality, are often incoherent, and demonstrate a remarkable degree of agreement on issues where they do take stances.
