# Mechanism Design for Large Language Models

arXiv: https://arxiv.org/abs/2310.10826 · HTML: https://mlsanity.com/paper/2310.10826/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2023.

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

- [Online Advertisements with LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.07601/) (2023)

## Abstract (arXiv)

We investigate auction mechanisms for AI-generated content, focusing on applications like ad creative generation. In our model, agents' preferences over stochastically generated content are encoded as large language models (LLMs). We propose an auction format that operates on a token-by-token basis, and allows LLM agents to influence content creation through single dimensional bids. We formulate two desirable incentive properties and prove their equivalence to a monotonicity condition on output aggregation. This equivalence enables a second-price rule design, even absent explicit agent valuation functions. Our design is supported by demonstrations on a publicly available LLM.