arXiv:2607.01557v1 Announce Type: new Abstract: Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios. People's individual personalities and concerns require tailored strategies rather than a one-size-fits-all approach. To address this challenge, we focus on a fire-rescue scenario in which an operator must persuade a resident to evacuate as a high-stakes persuasion domain and propose Dialogue Policy Selection (DiPS), a Q-learning framework to dynamically select persuasion strategies adapted to the evolving conversational context. Specifically, we train a critic,
The increasing capabilities and deployment of LLMs highlight the critical need for advanced persuasion and ethical interaction in real-world, high-stakes scenarios, which current models often fail at.
This research addresses a fundamental limitation of current AI—its inability to dynamically tailor persuasion strategies in sensitive, critical situations—which is essential for safe and effective agent deployment.
The development of frameworks like DiPS signifies a move towards more sophisticated, context-aware, and adaptive AI agents, capable of nuanced interaction in complex human environments, moving beyond rigid, generalized responses.
- · AI ethicists
- · Emergency services using AI
- · Companies deploying AI agents in sensitive roles
- · AI human-computer interaction researchers
- · Developers of one-size-fits-all AI persuasion models
- · AI systems lacking adaptive interaction capabilities
- · Organizations relying on rudimentary AI communication
AI agents will become more effective in critical human-facing roles requiring persuasion and dynamic adaptation.
Public trust and acceptance of AI in sensitive applications may increase as these systems demonstrate greater empathy and situational awareness.
The development of robust ethical guidelines and control mechanisms for AI persuasion could become a critical area of policy and regulation, impacting deployment across sectors.
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