从竞争到协作：设计LLMs与在线论坛之间的可持续机制
From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums
AI总结：
本文提出一个序列交互框架，使LLMs与论坛协作，解决生成式AI依赖论坛数据的悖论，通过模拟展示激励不一致问题，证明可持续知识共享的潜力。
摘要：
虽然生成式AI系统吸引用户离开(Q&A)论坛，但它们也依赖论坛产生的数据来提升性能。为解决这一悖论，我们提出一个交互框架，其中生成式AI系统向论坛提出问题，论坛可以发布其中一些问题。该框架捕捉了这种协作的多个复杂性，包括非货币交换、信息不对称和激励不一致。我们通过使用真实Stack Exchange数据和常用LLMs进行全面、数据驱动的模拟来实现该框架。我们实证地展示了激励不一致问题，但显示参与者在理想全信息场景下可以实现大约一半的效用。我们的结果突显了可持续协作的潜力，这种协作能有效保持AI系统与人类知识平台之间的知识共享。
英文摘要：
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.
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。
