用大模型设计激励机制，让Web3用户更愿意产出高质量内容。
LMM-Incentive: Large Multimodal Model-based Incentive Design for User-Generated Content in Web 3.0
- 用大模型构建合约理论框架，引导用户主动生成优质内容。
- 通过提示工程提升大模型对内容质量的评估能力，减少低质内容泛滥。
- 结合动态优化算法，在以太坊上实现可落地的激励方案，适合平台方参考。
Web 3.0作为下一代互联网，以去中心化为特征，强调价值表达与数据所有权。借助区块链与人工智能技术，用户得以创建、拥有并变现内容，推动用户生成内容（UGC）进入新阶段。然而，部分自利用户可能利用内容筛选机制缺陷，以低投入生成低质量内容，在信息不对称下获取平台奖励，损害系统性能。为此，本文提出LMM-Incentive，一种基于大多模态模型（LMM）的Web 3.0 UGC激励机制。通过构建基于LMM的契约理论模型，激励用户生成高质量内容，缓解信息不对称带来的逆向选择问题。为应对合同选定后的道德风险，采用LMM代理进行内容质量评估，结合提示工程提升评估效果。针对传统契约设计难以适应Web 3.0动态环境的问题，提出改进的基于专家混合（MoE）的近端策略优化（PPO）算法，实现最优契约设计。仿真结果表明，该算法在契约设计任务中优于多个基准方法。最终，将设计的契约部署于以太坊智能合约框架，验证了方案的有效性。
原文摘要 · Abstract (English)
Web 3.0 represents the next generation of the Internet, which is widely recognized as a decentralized ecosystem that focuses on value expression and data ownership. By leveraging blockchain and artificial intelligence technologies, Web 3.0 offers unprecedented opportunities for users to create, own, and monetize their content, thereby enabling User-Generated Content (UGC) to an entirely new level. However, some self-interested users may exploit the limitations of content curation mechanisms and generate low-quality content with less effort, obtaining platform rewards under information asymmetry. Such behavior can undermine Web 3.0 performance. To this end, we propose \textit{LMM-Incentive}, a novel Large Multimodal Model (LMM)-based incentive mechanism for UGC in Web 3.0. Specifically, we propose an LMM-based contract-theoretic model to motivate users to generate high-quality UGC, thereby mitigating the adverse selection problem from information asymmetry. To alleviate potential moral hazards after contract selection, we leverage LMM agents to evaluate UGC quality, which is the primary component of the contract, utilizing prompt engineering techniques to improve the evaluation performance of LMM agents. Recognizing that traditional contract design methods cannot effectively adapt to the dynamic environment of Web 3.0, we develop an improved Mixture of Experts (MoE)-based Proximal Policy Optimization (PPO) algorithm for optimal contract design. Simulation results demonstrate the superiority of the proposed MoE-based PPO algorithm over representative benchmarks in the context of contract design. Finally, we deploy the designed contract within an Ethereum smart contract framework, further validating the effectiveness of the proposed scheme.
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