标题： 从代理人到受托人：如何优化长期利益塑造了大语言模型中的偏见与对齐 显示英文标题

标题： From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM

摘要： 大型语言模型（LLMs）在预测调查回答和政策偏好方面表现出令人鼓舞的准确性，这增加了人们对它们在各个领域代表人类利益潜力的兴趣。 现有的大多数研究都集中在行为克隆上，有效地评估了模型复制个人表达偏好的能力。 借鉴政治代表理论，我们强调了一个未被充分探索的设计权衡：人工智能系统应该作为代理人，反映表达出的偏好，还是作为受托人，对什么最符合个人利益做出判断。 这种权衡与LLM奉承问题密切相关，其中模型可能会鼓励与用户短期偏好一致的行为或验证某些信念，但这会对用户的长期利益造成损害。 通过一系列模拟美国各种政策问题投票的实验，我们应用了一个时间效用框架，权衡短期和长期利益（模拟受托人角色），并将投票结果与行为克隆模型（模拟代理人）进行比较。 我们发现，侧重于长期利益的受托人风格预测会产生更符合专家共识的政策决策，但在缺乏明确共识的主题上，也显示出更大的模型默认立场偏差。 这些发现揭示了在设计代表人类利益的人工智能系统时的基本权衡。 代理人模型更好地保持了用户自主性，但可能偏离得到广泛支持的政策立场；而受托人模型可以在理解明确的问题上促进福利，但在主观主题上可能带来家长式作风和偏差。 显示英文摘要

摘要： Large language models (LLMs) have shown promising accuracy in predicting survey responses and policy preferences, which has increased interest in their potential to represent human interests in various domains. Most existing research has focused on behavioral cloning, effectively evaluating how well models reproduce individuals' expressed preferences. Drawing on theories of political representation, we highlight an underexplored design trade-off: whether AI systems should act as delegates, mirroring expressed preferences, or as trustees, exercising judgment about what best serves an individual's interests. This trade-off is closely related to issues of LLM sycophancy, where models can encourage behavior or validate beliefs that may be aligned with a user's short-term preferences, but is detrimental to their long-term interests. Through a series of experiments simulating votes on various policy issues in the U.S. context, we apply a temporal utility framework that weighs short and long-term interests (simulating a trustee role) and compare voting outcomes to behavior-cloning models (simulating a delegate). We find that trustee-style predictions weighted toward long-term interests produce policy decisions that align more closely with expert consensus on well-understood issues, but also show greater bias toward models' default stances on topics lacking clear agreement. These findings reveal a fundamental trade-off in designing AI systems to represent human interests. Delegate models better preserve user autonomy but may diverge from well-supported policy positions, while trustee models can promote welfare on well-understood issues yet risk paternalism and bias on subjective topics.