When to Ask a Question: Understanding Communication Strategies in Generative AI Tools
Charlotte Park, Kate Donahue, Manish Raghavan

TL;DR
This paper models user-LLM interactions to optimize information elicitation, balancing user burden and preference diversity, aiming to reduce bias and improve fairness in generative AI tools.
Contribution
It introduces a theoretical framework for balancing inference and elicitation in generative AI, supported by empirical validation.
Findings
Optimal information solicitation reduces bias in preference inference.
Elicitation strategies improve fairness without excessive user burden.
Model predictions align with empirical observations.
Abstract
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…
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