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

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.

💡 Research Summary

The paper tackles a paradox at the heart of today’s knowledge ecosystem: generative AI systems (LLMs) rely heavily on the high‑quality, human‑generated content of online Q&A forums for training, evaluation, and benchmarking, yet the same AI systems are drawing users away from those forums. Existing remedies—data‑access restrictions or monetary compensation—frame the relationship as adversarial and risk eroding community trust. Instead, the authors propose a non‑monetary, sequential collaboration framework in which an LLM submits a limited set of “hard” questions it cannot answer, and the forum curates which of these to publish.

The design rests on three guiding principles: (1) prohibit monetary transfers to preserve intrinsic motivations and autonomy of community members; (2) acknowledge and address the incentive misalignment between LLMs (which value questions that expose model uncertainty) and forums (which value questions that attract user engagement); and (3) model the interaction as an asymmetric‑information game where each side keeps its private utility functions hidden.

Formally, over T discrete rounds, Player G (the LLM provider) draws a candidate pool Qₜ of uncertain questions, selects a subset Aₜ of size ≤ M, and sends it to Player F (the forum). Player F applies a selection rule R, publishing at most K questions Sₜ = R(Aₜ). Each question q carries a deterministic utility u_G(q) for the LLM (e.g., learning gain) and u_F(q) for the forum (e.g., view count, votes). The cumulative utilities are additive over published sets. In the full‑information benchmark, both parties would jointly maximize the Nash product U_G(S)·U_F(S) for each round, yielding an optimal set Sₜ. However, achieving Sₜ requires full disclosure of utilities and the entire candidate pool, which is unrealistic.

To evaluate how well realistic, privacy‑preserving strategies recover the ideal utility, the authors introduce the Utility Recovery Rate (URR): URR_G = U_G(A,R)/U_G(S*) and URR_F = U_F(A,R)/U_F(S*). They prove that finding S*ₜ is NP‑hard, motivating the use of heuristics (e.g., utility‑ratio ranking, Lagrangian relaxation) to approximate the optimum.

Empirical evaluation uses real Stack Exchange data from several communities and multiple open‑source LLMs (GPT‑2, LLaMA, etc.). The simulations reveal a systematic misalignment: questions with high perplexity (high learning value for LLMs) often have low view counts, while high‑view questions provide little new information to the model. Despite this, the proposed asymmetric game recovers 46–52 % of the LLM’s potential learning utility and 56–66 % of the forum’s engagement utility—roughly half of the theoretical optimum—without any monetary exchange or full disclosure of private information. Moreover, when the forum’s selection rule R incorporates a weighted balance of both utilities rather than a pure popularity metric, URR improves further.

The contributions are threefold: (i) a realistic, non‑monetary collaboration design grounded in community‑centric principles; (ii) a game‑theoretic framework that captures asymmetric information and distinct utility functions for LLMs and forums; and (iii) data‑driven simulations that demonstrate substantial mutual gains are achievable even under strategic privacy constraints. The work suggests that policy makers and platform designers can foster sustainable AI‑human knowledge ecosystems by engineering incentive‑compatible mechanisms rather than relying on restrictive or financially driven solutions.