GenAI主动答错新领域问题，推动用户去人工论坛，长远提升自身收益与用户体验。
Selective Response Strategies for GenAI
- GenAI对新兴技术问题故意给出保守答案，引导用户转向人工论坛
- 该策略可长期提升数据生成效率，使系统收益和用户福利双增长
- 提出算法优化与监管条件，确保策略在社会福利约束下有效
生成式人工智能（GenAI）的兴起显著影响了依赖人工的高质量信息平台（如Stack Overflow），而这些平台又是训练GenAI的关键数据来源，形成负向反馈循环。本文提出一种新策略——选择性回应：GenAI在面对新兴话题和新技术时，主动提供不准确或保守的回答，从而引导用户前往人工论坛。我们证明，这种策略能产生正向累积效应，长期提升GenAI的收益和用户福利。从算法角度，提出一种近似最优方法，在社会福利约束下最大化收益；从监管角度，推导出选择性回应改善整体福利的充要条件。
原文摘要 · Abstract (English)
The rise of Generative AI (GenAI) has significantly impacted human-based forums like Stack Overflow, which are essential for generating high-quality data. This creates a negative feedback loop, hindering the development of GenAI systems, which rely on such data to provide accurate responses. In this paper, we provide a possible remedy: A novel strategy we call selective response. Selective response implies that GenAI could strategically provide inaccurate (or conservative) responses to queries involving emerging topics and novel technologies, thereby driving users to use human-based forums like Stack Overflow. We show that selective response can potentially have a compounding effect on the data generation process, increasing both GenAI's revenue and user welfare in the long term. From an algorithmic perspective, we propose an approximately optimal approach to maximize GenAI's revenue under social welfare constraints. From a regulatory perspective, we derive sufficient and necessary conditions for selective response to improve welfare improvements.
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