Summary of Online Prompt Pricing Based on Combinatorial Multi-armed Bandit and Hierarchical Stackelberg Game, by Meiling Li et al.
Online Prompt Pricing based on Combinatorial Multi-Armed Bandit and Hierarchical Stackelberg Game
by Meiling Li, Hongrun Ren, Haixu Xiong, Zhenxing Qian, Xinpeng Zhang
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel prompt trading scenario, prompt bundle trading (PBT), is proposed in this paper to create a more flexible and diverse pricing mechanism. Building on combinatorial multi-armed bandit (CMAB) and three-stage hierarchical Stackelburg (HS) game models, the online pricing mechanism considers profits for consumers, platforms, and sellers simultaneously, achieving satisfaction for all three participants. The pricing issue is broken down into two steps: unknown category selection and incentive strategy optimization. Unlike fixed pricing modes, PBT offers a more adaptable approach, aligning with real-world transaction needs. Experimental results on a simulated text-to-image dataset demonstrate the effectiveness of the algorithm, providing a feasible price-setting standard for prompt marketplaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prompt trading models are used to create a pricing mechanism that works for consumers, platforms, and sellers. This new system is called prompt bundle trading (PBT). It’s based on two ideas: combinatorial multi-armed bandit (CMAB) and three-stage hierarchical Stackelburg (HS) game. The goal is to make sure everyone gets what they want – the consumer, platform, and seller. To do this, the pricing mechanism has two parts: picking the best categories and figuring out the right strategy for each person. This new way of pricing is more flexible than the old way and works better in real-life situations. The team tested it with a pretend text-to-image dataset and saw that it worked well. |
Keywords
» Artificial intelligence » Optimization » Prompt
