LMaaS：探索作为服务的大模型在通信中的定价策略
LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication
AI总结：
本文针对大模型即服务（LMaaS）在智能通信中的交易与定价难题，将其建模为两步 Stackelberg 博弈，提出 IMP 算法优化卖方定价及 RSR 算法优化客户选择，实验证明该方法有效且鲁棒。
摘要：
下一代通信被设想为智能通信，能够替代传统的符号通信，在这种通信中，考虑信源和信道的、高度浓缩的语义信息将被高效地提取和传输。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，并推动了其在不久的将来的实际部署。鉴于这些多模态大语言模型“一次训练，广泛使用”的特点，我们认为按需付费的服务模式将适用于此场景，称为大模型即服务（LMaaS）。然而，在异构且动态的客户环境中，交易和定价问题相当复杂，这使得定价优化问题在寻求现有解决方案时极具挑战性。在本文中，我们旨在填补这一空白，并将 LMaaS 市场交易表述为两步的 Stackelberg 博弈。在第一步中，我们优化卖方的定价决策，并提出了一种迭代模型定价（IMP）算法，该算法通过推理客户未来的租赁决策来迭代优化大模型的价格，能够实现近最优的定价方案。在第二步中，我们通过设计鲁棒选择与租赁（RSR）算法来优化客户的选择决策，严格的数学证明保证其最优性。大量实验证实了我们算法的有效性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
