# Sponsored Question Answering

arXiv: https://arxiv.org/abs/2407.04471 · HTML: https://mlsanity.com/paper/2407.04471/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2024.

## Built on by (most cited first, 7 of 7)

- [Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers](https://mlsanity.com/paper/2502.09053/) (2025)
- [LLM-Auction: Generative Auction towards LLM-Native Advertising](https://mlsanity.com/paper/2512.10551/) (2025)
- [Ad Insertion in LLM-Generated Responses](https://mlsanity.com/paper/2601.19435/) (2026)
- [MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution](https://mlsanity.com/paper/2512.05958/) (2025)
- [NaiAD: Initiate Data-Driven Research for LLM Advertising](https://mlsanity.com/paper/2605.09918/) (2026)
- [LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots](https://mlsanity.com/paper/2605.16474/) (2026)
- [Sponsored Questions and How to Auction Them](https://mlsanity.com/paper/2512.03975/) (2025)

## Abstract (arXiv)

The potential move from search to question answering (QA) ignited the question of how should the move from sponsored search to sponsored QA look like. We present the first formal analysis of a sponsored QA platform. The platform fuses an organic answer to a question with an ad to produce a so called em sponsored answer. Advertisers then bid on their sponsored answers. Inspired by Generalized Second Price Auctions (GSPs), the QA platform selects the winning advertiser, sets the payment she pays, and shows the user the sponsored answer. We prove an array of results. For example, advertisers are incentivized to be truthful in their bids; i.e., set them to their true value of the sponsored answer. The resultant setting is stable with properties of VCG auctions.