# Auctions with LLM Summaries

arXiv: https://arxiv.org/abs/2404.08126 · HTML: https://mlsanity.com/paper/2404.08126/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation](https://mlsanity.com/paper/2212.07981/) (2022)
- [Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango](https://mlsanity.com/paper/2209.07686/) (2022)
- [Sponsored Search Auctions with Rich Ads](https://mlsanity.com/paper/1701.05948/) (2017)
- [Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions](https://mlsanity.com/paper/2206.02948/) (2022)

## Built on by (most cited first, 24 of 24)

- [Truthful Aggregation of LLMs with an Application to Online Advertising](https://mlsanity.com/paper/2405.05905/) (2024)
- [Ad Auctions for LLMs via Retrieval Augmented Generation](https://mlsanity.com/paper/2406.09459/) (2024)
- [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)
- [Online Learning from Strategic Human Feedback in LLM Fine-Tuning](https://mlsanity.com/paper/2412.16834/) (2024)
- [Ad Insertion in LLM-Generated Responses](https://mlsanity.com/paper/2601.19435/) (2026)
- [The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration](https://mlsanity.com/paper/2604.17139/) (2026)
- [GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing](https://mlsanity.com/paper/2509.14221/) (2025)
- [MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution](https://mlsanity.com/paper/2512.05958/) (2025)
- [LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots](https://mlsanity.com/paper/2605.16474/) (2026)
- [TeamCMU at Touch'e: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search](https://mlsanity.com/paper/2507.00509/) (2025)
- [Incentive-Aligned Multi-Source LLM Summaries](https://mlsanity.com/paper/2509.25184/) (2025)
- [Generating Fair Consensus Statements with Social Choice on Token-Level MDPs](https://mlsanity.com/paper/2510.14106/) (2025)
- [Sponsored Questions and How to Auction Them](https://mlsanity.com/paper/2512.03975/) (2025)
- [Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models](https://mlsanity.com/paper/2604.06263/) (2026)
- [Generative AI Advertising as a Problem of Trustworthy Commercial Intervention](https://mlsanity.com/paper/2605.18673/) (2026)
- [Error-Aware Reverse Auction Mechanism for Large Language Model Routing](https://mlsanity.com/paper/2608.12719/) (2026)
- [LLMs for Resource Allocation: A Participatory Budgeting Approach to Inferring Preferences](https://mlsanity.com/paper/2508.06060/) (2025)
- [Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives](https://mlsanity.com/paper/2512.01179/) (2025)
- [TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents](https://mlsanity.com/paper/2605.10440/) (2026)
- [Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing](https://mlsanity.com/paper/2605.24052/) (2026)
- [Decentralized Aggregation of LLM Predictions via Wagering Mechanisms](https://mlsanity.com/paper/2607.04389/) (2026)
- [AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution](https://mlsanity.com/paper/2608.13833/) (2026)
- [Inference Auctions](https://mlsanity.com/paper/2609.40070/) (2026)

## Abstract (arXiv)

We study an auction setting in which bidders bid for placement of their content within a summary generated by a large language model (LLM), e.g., an ad auction in which the display is a summary paragraph of multiple ads. This generalizes the classic ad settings such as position auctions to an LLM generated setting, which allows us to handle general display formats. We propose a novel factorized framework in which an auction module and an LLM module work together via a prediction model to provide welfare maximizing summary outputs in an incentive compatible manner. We provide a theoretical analysis of this framework and synthetic experiments to demonstrate the feasibility and validity of the system together with welfare comparisons.