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.

翻译：下一代通信被设想为智能通信，将取代传统符号通信，其中融合信源与信道的深度语义信息将被高效提取与传输。近期流行的GPT4等大模型以及不断发展的学习技术为智能通信奠定了坚实基础，并推动了其在近期内的实际部署。鉴于这些多模态大语言模型“一次训练、广泛使用”的特性，我们认为按需付费的服务模式在此场景下具有适用性，即大模型即服务（LMaaS）。然而，在异质且动态变化的客户环境下，交易与定价问题极为复杂，使得定价优化在寻求即时解决方案时面临挑战。本文旨在填补这一空白，将LMaaS市场交易建模为两阶段斯塔克尔伯格博弈。第一阶段，我们优化卖方定价决策，提出迭代模型定价（IMP）算法，通过推理客户未来租赁决策来迭代优化大模型定价，该算法能够获得近似最优的定价方案。第二阶段，我们通过设计鲁棒选择与租赁（RSR）算法优化客户选择决策，该算法具有严格理论证明的全局最优性。大量实验验证了我们算法的有效性与鲁棒性。