arXiv:2606.02754v1 Announce Type: new Abstract: Personalization is a crucial capability of modern language agents. However, current research primarily positions personalized agents as passive responders to user preferences, limiting their ability to interact with users and provide suggestions or guidance proactively. To systematically evaluate such proactive personalization in realistic interactions, we propose $\Psi$-Bench, a benchmark for assessing LLMs' ability to influence realistic users through conversation. We design three real-world interaction scenarios that involve persuasion in $\Ps
The proliferation of language agents necessitates robust evaluation benchmarks to ensure their capabilities align with desired proactive, personalized interaction and influence.
This development is crucial for understanding and controlling the persuasive capabilities of AI, impacting areas from marketing to public dialogue.
The ability to systematically assess and improve the persona-sensitive influencing capabilities of LLMs becomes more formalized, enabling more sophisticated and ethical agent design.
- · AI developers focused on personalized user experience
- · Ethicists and regulatory bodies addressing AI persuasion
- · Researchers in human-computer interaction
- · Industries relying on persuasive digital agents
- · Platforms with easily manipulable users
- · Traditional marketing unqualified for AI influence
- · Lack of regulatory oversight
Improved benchmarks lead to more sophisticated and ethically sound persuasive AI agents.
Increased adoption of AI agents for complex influencing tasks across various sectors, from healthcare to customer service.
Societal debates intensify regarding the ethics and regulation of AI-driven persuasion and its impact on human autonomy.
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Read at arXiv cs.LG