Generative AI models differ from traditional machine learning tools in that they allow users to provide as much or as little information as they choose in their inputs. This flexibility often leads users to omit certain details, relying on the models to infer and fill in under-specified information based on distributional knowledge of user preferences. Such inferences may privilege majority viewpoints and disadvantage users with atypical preferences, raising concerns about fairness. Unlike more traditional recommender systems, LLMs can explicitly solicit more information from users through natural language. However, while directly eliciting user preferences could increase personalization and mitigate inequality, excessive querying places a burden on users who value efficiency. We develop a stylized model of user-LLM interaction and develop an objective that captures tradeoff between user burden and preference representation. Building on the observation that individual preferences are often correlated, we analyze how AI systems should balance inference and elicitation, characterizing the optimal amount of information to solicit before content generation. Ultimately, we show that information elicitation can mitigate the systematic biases of preference inference, enabling the design of generative tools that better incorporate diverse user perspectives while maintaining efficiency. We complement this theoretical analysis with an empirical evaluation illustrating the model's predictions and exploring their practical implications.

翻译：生成式人工智能模型与传统机器学习工具的区别在于，它们允许用户在输入中提供任意多或少的信息。这种灵活性常导致用户省略某些细节，依赖模型根据用户偏好的分布知识推断并补充未充分说明的信息。此类推断可能偏向多数观点，使具有非典型偏好的用户处于不利地位，从而引发公平性问题。与传统推荐系统不同，大型语言模型能够通过自然语言显式向用户索取更多信息。然而，尽管直接询问用户偏好可提升个性化并减轻不平等，但过度询问会给重视效率的用户带来负担。我们构建了一个用户-大型语言模型交互的简化模型，并提出一个捕捉用户负担与偏好表征之间权衡的目标函数。基于个体偏好通常具有关联性的观察，我们分析了人工智能系统应如何平衡推断与信息获取，刻画了内容生成前应询问的最优信息量。最终，研究表明信息获取能够缓解偏好推断的系统性偏差，从而设计出在保持效率的同时更好整合多样化用户视角的生成工具。我们通过实证评估补充了这一理论分析，展示了模型的预测结果并探讨了其实践意义。