From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums

2026-02-04

AI Summary

This study addresses the tension between large language models (LLMs) and human-driven knowledge platforms: while LLMs increasingly rely on data from online Q&A forums to enhance performance, their use diverts user engagement, threatening platform sustainability and creating misaligned incentives and ecological imbalance in knowledge production. To reconcile this conflict, the work proposes a sequential interaction framework that models the asymmetric collaboration between LLMs and forums—where the LLM submits questions and the forum selectively publishes them—coordinating interests through a non-monetary exchange mechanism under information asymmetry. Using real-world Stack Exchange data and mainstream LLMs, the authors construct a multi-agent simulation to empirically confirm the existence of incentive misalignment and demonstrate that the proposed mechanism enables both parties to achieve approximately 50% of the utility attainable under ideal full-information conditions, thereby establishing the feasibility of sustainable collaboration.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Mechanism Design
• Natural Language Processing: (Large) Language Models

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Generative AI
• online forums
• incentive misalignment
• sustainable collaboration
• asymmetric information

Innovation

• sequential interaction
• incentive alignment
• generative AI
• online Q&A forums
• sustainable collaboration