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Auctions with LLM Summaries (2404.08126v1)

Published 11 Apr 2024 in cs.GT and cs.AI

Abstract: We study an auction setting in which bidders bid for placement of their content within a summary generated by a 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.

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References (27)
  1. Simple mechanisms for welfare maximization in rich advertising auctions. Advances in Neural Information Processing Systems, 35:28280–28292, 2022.
  2. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687, 2023.
  3. Sponsored search auctions with rich ads. In Proceedings of the 26th International Conference on World Wide Web, WWW ’17, page 43–51, Republic and Canton of Geneva, CHE, 2017. International World Wide Web Conferences Steering Committee. ISBN 9781450349130. doi: 10.1145/3038912.3052703. URL https://doi.org/10.1145/3038912.3052703.
  4. Mechanism design for large language models, 2023.
  5. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review, 97(1):242–259, March 2007.
  6. Recommender systems in the era of large language models (llms). arXiv preprint arXiv:2307.02046, 2023.
  7. Online advertisements with llms: Opportunities and challenges, 2023.
  8. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems, pages 299–315, 2022.
  9. Google. Supercharging search with generative ai. https://blog.google/products/search/generative-ai-search/, 2023.
  10. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199–22213, 2022.
  11. Retrieval-augmented generation for knowledge-intensive nlp tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, 2020. ISBN 9781713829546.
  12. Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain, July 2004. Association for Computational Linguistics. URL https://aclanthology.org/W04-1013.
  13. Revisiting the gold standard: Grounding summarization evaluation with robust human evaluation. arXiv preprint arXiv:2212.07981, 2022.
  14. Text and patterns: For effective chain of thought, it takes two to tango. arXiv preprint arXiv:2209.07686, 2022.
  15. Self-refine: Iterative refinement with self-feedback. arXiv preprint arXiv:2303.17651, 2023.
  16. Microsoft. Ai powered bing. https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/, 2023.
  17. Roger B Myerson. Optimal auction design. Mathematics of operations research, 6(1):58–73, 1981.
  18. Show your work: Scratchpads for intermediate computation with language models. arXiv preprint arXiv:2112.00114, 2021.
  19. Reserve prices in internet advertising auctions: A field experiment. In Proceedings of the 12th ACM conference on Electronic commerce, pages 59–60, 2011.
  20. Hal R. Varian. Position auction. International Journal of Industrial Organization, 2006.
  21. William Vickrey. Counterspeculation, auctions, and competitive sealed tenders. The Journal of finance, 16(1):8–37, 1961.
  22. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. arXiv preprint arXiv:2305.04091, 2023.
  23. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022.
  24. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022.
  25. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023.
  26. Benchmarking large language models for news summarization. Transactions of the Association for Computational Linguistics, 12:39–57, 2024.
  27. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625, 2022.
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