LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication

• URL: http://arxiv.org/abs/2401.02675v1
• Date: Fri, 5 Jan 2024 07:19:19 GMT
• Title: LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication
• Authors: Panlong Wu, Qi Liu, Yanjie Dong, Fangxin Wang
• Abstract summary: We argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LM) We propose an Iterative Model Pricing (IMP) algorithm that optimize the prices of large models iteratively by reasoning customers' future rental decisions. In the second step, we optimize customers' selection decisions by designing a robust selecting and renting algorithm.
• Score: 11.337245234301857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The next generation of communication is envisioned to be intelligent communication, that can replace traditional symbolic communication, where highly condensed semantic information considering both source and channel will be extracted and transmitted with high efficiency. The recent popular large models such as GPT4 and the boosting learning techniques lay a solid foundation for the intelligent communication, and prompt the practical deployment of it in the near future. Given the characteristics of "training once and widely use" of those multimodal large language models, we argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LMaaS). However, the trading and pricing problem is quite complex with heterogeneous and dynamic customer environments, making the pricing optimization problem challenging in seeking on-hand solutions. In this paper, we aim to fill this gap and formulate the LMaaS market trading as a Stackelberg game with two steps. In the first step, we optimize the seller's pricing decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes the prices of large models iteratively by reasoning customers' future rental decisions, which is able to achieve a near-optimal pricing solution. In the second step, we optimize customers' selection decisions by designing a robust selecting and renting (RSR) algorithm, which is guaranteed to be optimal with rigorous theoretical proof. Extensive experiments confirm the effectiveness and robustness of our algorithms.

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