Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models
Summary: arXiv:2604.06263v1 Announce Type: cross
Abstract
Generative advertising in large language model (LLM) responses requires optimizing sponsorship configurations under two strict constraints: the strategic behavior of advertisers and the high cost of stochastic generations. To address this, we propose the Incentive-Aware Multi-Fidelity Mechanism (IAMFM), a unified framework coupling Vickrey-Clarke-Groves (VCG) incentives with Multi-Fidelity Optimization to maximize expected social welfare.
Introduction
The rise of large language models has transformed the landscape of digital advertising, but it has also introduced complex challenges in managing advertiser incentives and optimizing costs. Traditional methods often fail to account for the nuances of advertiser behavior and the stochastic nature of language model outputs. This necessitates a more sophisticated approach to generative advertising.
Proposed Framework
To tackle these challenges, we introduce the Incentive-Aware Multi-Fidelity Mechanism (IAMFM), which integrates VCG incentives with Multi-Fidelity Optimization. This framework aims to:
- Maximize expected social welfare in advertising configurations.
- Ensure that the strategic behaviors of advertisers are effectively accounted for.
- Minimize the costs associated with stochastic generations in LLMs.
Algorithmic Instantiations
Our analysis presents two algorithmic instantiations of IAMFM:
- Elimination-based Instantiation: This approach systematically eliminates suboptimal configurations, refining the set of potential sponsorships.
- Model-based Instantiation: Leveraging predictive models, this method optimizes configurations based on anticipated advertiser behavior and responses.
Both methods reveal significant budget-dependent performance trade-offs, highlighting the need for careful selection based on specific advertising goals.
Active Counterfactual Optimization
To enhance the computational feasibility of VCG mechanisms, we introduce Active Counterfactual Optimization. This “warm-start” approach allows for the reuse of optimization data, significantly speeding up payment calculations and making the system more efficient.
Guarantees and Performance
We provide formal guarantees for approximate strategy-proofness and individual rationality, ensuring that our approach is both reliable and fair. Our experimental results demonstrate that IAMFM consistently outperforms single-fidelity baselines across diverse budget scenarios, confirming its efficacy in real-world applications.
Conclusion
The Incentive-Aware Multi-Fidelity Mechanism represents a significant advancement in the realm of generative advertising within large language models. By aligning incentives and optimizing configurations under budget constraints, IAMFM paves the way for more effective and efficient advertising strategies in an increasingly competitive digital marketplace.
Future Work
Moving forward, we aim to explore additional refinements to the IAMFM framework, including its application to other domains of machine learning and optimizing generative processes beyond advertising.