Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Models
arXiv:2503.01829v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) demonstrate persuasive capabilities that rival human-level persuasion. While these capabilities can be used for social good, they also present risks of potential misuse. Beyond the concern of how LLMs persuade others, their own susceptibility to persuasion poses a critical alignment challenge, raising questions about robustness, safety, and adherence to ethical principles. To study these dynamics, we introduce Persuade Me If You Can (PMIYC), an automated framework for evaluating persuasiveness and susceptibi
The rapid advancement of large language models necessitates immediate research into their persuasive capabilities and vulnerabilities to ensure responsible deployment and alignment.
Understanding LLM persuasiveness and susceptibility is critical for mitigating misuse risks, ensuring model alignment with human values, and developing robust safety protocols.
The introduction of a standardized framework for evaluating LLM persuasion will enable systematic study and benchmarking, shifting from anecdotal observations to empirical analysis.
- · AI Safety Researchers
- · Ethical AI Developers
- · Regulatory Bodies
- · Malicious Actors
- · Unaccountable AI Developers
- · Misinformation Propagators
The framework will allow for empirical measurement of how LLMs persuade and are persuaded, identifying vulnerabilities.
This understanding will inform the development of more resilient and aligned AI systems, reducing risks from adversarial persuasion.
Improved LLM robustness could lead to more trustworthy AI assistants and agents, but also more sophisticated and harder-to-detect forms of misuse.
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Read at arXiv cs.LG