From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums
This addresses the sustainability problem for online forums and AI developers, offering an incremental solution to foster collaboration.
The paper tackles the paradox where Generative AI systems both compete with and rely on online forums for data, proposing a sequential interaction framework to enable collaboration. Using simulations with real Stack Exchange data and LLMs, they show that despite incentive misalignment, players can achieve roughly half the utility of an ideal full-information scenario.
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.