Selective Response Strategies for GenAI

2025-02-02

AI Summary

Widespread GenAI adoption has reduced original content on Q&A platforms like Stack Overflow, triggering a negative feedback loop: declining data quality → degraded training data → diminished generative performance. Method: We propose a “strategic non-response” paradigm wherein GenAI deliberately provides conservative or imprecise answers to emerging-technology questions, thereby incentivizing users to seek solutions in human-moderated communities and stimulating high-quality data production. We formulate the first approximate-optimal decision framework jointly optimizing platform revenue and social welfare, integrating dynamic programming, mechanism design, and game-theoretic modeling under explicit social-welfare constraints. Contribution/Results: We theoretically prove that this strategy yields cumulative gains in both long-term platform revenue and user welfare. Empirical simulations demonstrate a 37% improvement in high-quality data generation efficiency and a 22% increase in user retention rate.

Technology Category

• Natural Language Processing: Question Answering
• Multiagent Systems: Mechanism Design
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Generative AI
• Data Quality
• User-generated Content

Innovation

• Selective Answering Strategy
• Generative AI Optimization
• Platform Data Quality Enhancement