Truthful Aggregation of LLMs with an Application to Online Advertising
- URL: http://arxiv.org/abs/2405.05905v3
- Date: Wed, 26 Jun 2024 06:29:32 GMT
- Title: Truthful Aggregation of LLMs with an Application to Online Advertising
- Authors: Ermis Soumalias, Michael J. Curry, Sven Seuken,
- Abstract summary: Large Language Models (LLMs) are being integrated into online platforms' services.
This makes revenue generation from LLM-generated content the next major challenge in online advertising.
We introduce an auction mechanism for this problem that operates without LLM fine-tuning.
We show that our mechanism significantly boosts advertiser value and platform revenue, with low computational overhead.
- Score: 11.552000005640203
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Online platforms generate hundreds of billions of dollars in revenue per year by showing advertisements alongside their own content. Currently, these platforms are integrating Large Language Models (LLMs) into their services. This makes revenue generation from LLM-generated content the next major challenge in online advertising. We consider a scenario where advertisers aim to influence the responses of an LLM to align with their interests, while platforms seek to maximize advertiser value and ensure user satisfaction. We introduce an auction mechanism for this problem that operates without LLM fine-tuning or access to model weights and provably converges to the output of the optimally fine-tuned LLM for the platform's objective as computational resources increase. Our mechanism ensures that truthful reporting is a dominant strategy for advertisers and it aligns each advertiser's utility with their contribution to social welfare - an essential feature for long-term viability. Additionally, it can incorporate contextual information about the advertisers, significantly accelerating convergence. Via experiments with a publicly available LLM, we show that our mechanism significantly boosts advertiser value and platform revenue, with low computational overhead. While our motivating application is online advertising, our mechanism can be applied in any setting with monetary transfers, making it a general-purpose solution for truthfully aggregating the preferences of self-interested agents over LLM-generated replies.
Related papers
- Ad Auctions for LLMs via Retrieval Augmented Generation [12.9128551468564]
This paper introduces novel auction mechanisms for ad allocation and pricing within the textual outputs of large language models (LLMs)
We propose a segment auction where an ad is probabilistically retrieved for each discourse segment according to its bid and relevance, following the RAG framework.
We show that our auction maximizes logarithmic social welfare, a new notion of welfare that balances allocation efficiency and fairness, and we characterize the associated incentive-compatible pricing rule.
arXiv Detail & Related papers (2024-06-12T22:05:51Z) - Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
We introduce AlphaLLM for the self-improvements of Large Language Models.
It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop.
Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
arXiv Detail & Related papers (2024-04-18T15:21:34Z) - Scaling Up LLM Reviews for Google Ads Content Moderation [22.43127685744644]
Large language models (LLMs) are powerful tools for content moderation, but their inference costs and latency make them prohibitive for casual use on large datasets.
This study proposes a method for scaling up LLM reviews for content in Google Ads.
arXiv Detail & Related papers (2024-02-07T23:47:02Z) - Making Large Language Models Better Knowledge Miners for Online
Marketing with Progressive Prompting Augmentation [34.37733369078883]
We propose PAIR, a novel Progressive prompting Augmented mIning fRamework for harvesting marketing-oriented knowledge graph with LLMs.
In particular, we reduce the pure relation generation to an LLM based adaptive relation filtering process through the knowledge-empowered prompting technique.
In terms of online serving, we specialize in a small and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality corpus provided by a strong teacher-LLM.
arXiv Detail & Related papers (2023-12-08T03:44:09Z) - Online Advertisements with LLMs: Opportunities and Challenges [51.96140910798771]
This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems.
We delve into essential requirements including privacy, latency, reliability as well as the satisfaction of users and advertisers that such a system must fulfill.
arXiv Detail & Related papers (2023-11-11T02:13:32Z) - Survey on Factuality in Large Language Models: Knowledge, Retrieval and
Domain-Specificity [61.54815512469125]
This survey addresses the crucial issue of factuality in Large Language Models (LLMs)
As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
arXiv Detail & Related papers (2023-10-11T14:18:03Z) - Check Your Facts and Try Again: Improving Large Language Models with
External Knowledge and Automated Feedback [127.75419038610455]
Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks.
This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
arXiv Detail & Related papers (2023-02-24T18:48:43Z) - A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in
Online Advertising [53.636153252400945]
We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies.
Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
arXiv Detail & Related papers (2021-06-11T08:07:14Z) - Modeling Influencer Marketing Campaigns In Social Networks [2.0303656145222857]
More than 3.8 billion people around the world actively use social media.
In this work, we present an agent-based model (ABM) that can simulate the dynamics of influencer advertizing campaigns.
arXiv Detail & Related papers (2021-06-03T11:01:06Z) - Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential
Advertising [52.3825928886714]
We formulate the sequential advertising strategy optimization as a dynamic knapsack problem.
We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space.
To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
arXiv Detail & Related papers (2020-06-29T18:50:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.