提出AI与问答论坛协作机制，缓解数据依赖矛盾。
From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums
- 设计顺序交互框架，让AI向论坛提问题并分享部分成果。
- 实测显示双方可获理想状态一半的收益，验证合作可行性。
- 适合关注AI与知识平台共生关系的研究者参考。
生成式AI（GenAI）系统在吸引用户离开问答论坛的同时，又依赖这些论坛产生的数据来提升自身性能，形成悖论。为此，我们提出一种顺序互动框架：由GenAI系统向论坛提出问题，论坛可选择发布其中一部分。该框架捕捉了非货币交换、信息不对称和激励错配等复杂特性。我们基于真实的Stack Exchange数据和常用LLM进行了全面的数据驱动模拟。实验表明，尽管存在激励错配，但各方仍能实现理想全信息情形下约一半的效用。结果揭示了生成式AI与人类知识平台之间可持续协作的潜力，有助于维持有效的知识共享生态。
原文摘要 · Abstract (English)
While Generative AI (GenAI) systems draw users away from (Q&A) forums, they also depend on the very data those forums produce to improve their performance. Addressing this paradox, we propose a framework of sequential interaction, in which a GenAI system proposes questions to a forum that can publish some of them. Our framework captures several intricacies of such a collaboration, including non-monetary exchanges, asymmetric information, and incentive misalignment. We bring the framework to life through comprehensive, data-driven simulations using real Stack Exchange data and commonly used LLMs. We demonstrate the incentive misalignment empirically, yet show that players can achieve roughly half of the utility in an ideal full-information scenario. Our results highlight the potential for sustainable collaboration that preserves effective knowledge sharing between AI systems and human knowledge platforms.
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