Selective Response Strategies for GenAI

TL;DR

This paper proposes a selective response strategy for GenAI that intentionally provides conservative answers on emerging topics to encourage human forum engagement, aiming to enhance long-term data quality and system development.

Contribution

It introduces a novel selective response approach for GenAI, including an algorithmic method and regulatory conditions to optimize revenue and social welfare.

Findings

Selective response can increase GenAI revenue and user welfare.

The proposed approach balances accuracy and conservatism effectively.

Regulatory conditions ensure welfare improvements.

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.