Generation models have shown promising performance in various tasks, making trading around machine learning models possible. In this paper, we aim at a novel prompt trading scenario, prompt bundle trading (PBT) system, and propose an online pricing mechanism. Based on the combinatorial multi-armed bandit (CMAB) and three-stage hierarchical Stackelburg (HS) game, our pricing mechanism considers the profits of the consumer, platform, and seller, simultaneously achieving the profit satisfaction of these three participants. We break down the pricing issue into two steps, namely unknown category selection and incentive strategy optimization. The former step is to select a set of categories with the highest qualities, and the latter is to derive the optimal strategy for each participant based on the chosen categories. Unlike the existing fixed pricing mode, the PBT pricing mechanism we propose is more flexible and diverse, which is more in accord with the transaction needs of real-world scenarios. We test our method on a simulated text-to-image dataset. The experimental results demonstrate the effectiveness of our algorithm, which provides a feasible price-setting standard for the prompt marketplaces.

翻译：生成模型在各类任务中展现出优异性能，使得围绕机器学习模型的交易成为可能。本文针对一种新颖的提示交易场景——提示组合交易系统，提出一种在线定价机制。该机制基于组合多臂老虎机与三阶段分层斯塔克尔伯格博弈，同时考量消费者、平台与销售方的收益，实现三方参与者的利润满意度。我们将定价问题分解为两个步骤：未知类别选择与激励策略优化。前者旨在选择具有最高质量的一组类别，后者则基于所选类别推导各参与者的最优策略。相较于现有固定定价模式，本文提出的提示组合交易定价机制更具灵活性与多样性，更符合实际场景的交易需求。我们在模拟文本到图像数据集上验证了所提方法，实验结果表明该算法的有效性，为提示交易市场提供了可行的定价标准。