Selective Response Strategies for GenAI
This addresses the negative feedback loop in GenAI development for users and developers reliant on human-generated data, though it is an incremental approach building on existing strategies.
The paper tackles the problem of Generative AI (GenAI) hindering human forums like Stack Overflow, which are crucial for generating high-quality data, by proposing a selective response strategy where GenAI provides inaccurate or conservative responses to queries on emerging topics to drive users to these forums, potentially increasing GenAI's revenue and user welfare in the long term.
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.