# Sponsored Questions and How to Auction Them

arXiv: https://arxiv.org/abs/2512.03975 · HTML: https://mlsanity.com/paper/2512.03975/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Truthful Aggregation of LLMs with an Application to Online Advertising](https://mlsanity.com/paper/2405.05905/) (2024)
- [Auctions with LLM Summaries](https://mlsanity.com/paper/2404.08126/) (2024)
- [Ad Auctions for LLMs via Retrieval Augmented Generation](https://mlsanity.com/paper/2406.09459/) (2024)
- [Online Advertisements with LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.07601/) (2023)
- [Sponsored Question Answering](https://mlsanity.com/paper/2407.04471/) (2024)
- [Data-Driven Mechanism Design: Jointly Eliciting Preferences and Information](https://mlsanity.com/paper/2412.16132/) (2024)
- [Ads in Conversations](https://mlsanity.com/paper/2403.11022/) (2024)

## Built on by (most cited first, 1 of 1)

- [Selective Elicitation as a Commercial Influence Channel: A Reproducible Synthetic Shopping-Agent Stress Test](https://mlsanity.com/paper/2609.36614/) (2026)

## Abstract (arXiv)

Online platforms connect users with relevant products and services using ads. A key challenge is that a user's search query often leaves their true intent ambiguous. Typically, platforms passively predict relevance based on available signals and in some cases offer query refinements. The shift from traditional search to conversational AI provides a new approach. When a user's query is ambiguous, a Large Language Model (LLM) can proactively offer several clarifying follow-up prompts. In this paper we consider the following: what if some of these follow-up prompts can be ``sponsored,'' i.e., selected for their advertising potential. How should these ``suggestion slots'' be allocated? And, how does this new mechanism interact with the traditional ad auction that might follow? This paper introduces a formal model for designing and analyzing these interactive platforms. We use this model to investigate a critical engineering choice: whether it is better to build an end-to-end pipeline that jointly optimizes the user interaction and the final ad auction, or to decouple them into separate mechanisms for the suggestion slots and another for the subsequent ad slot. We show that the VCG mechanism can be adopted to jointly optimize the sponsored suggestion and the ads that follow; while this mechanism is more complex, it achieves outcomes that are efficient and truthful. On the other hand, we prove that the simple-to-implement modular approach suffers from strategic inefficiency: its Price of Anarchy is unbounded.