Persuasion Dynamics: LLMs' Tug-of-War with Influence

Large Language Models (LLMs) are stepping into uncharted territories of persuasion. Their capacity to influence rivals human-level persuasion skills. While this opens up avenues for social good, it raises concerns about potential misuse and ethical dilemmas. The susceptibility of these models to persuasion is an alignment challenge, one that demands urgent attention.

Introducing PMIYC

Enter Persuade Me If You Can (PMIYC), an innovative framework designed to assess persuasion dynamics within LLMs. This automated setup saves time and resources typically expended on human annotation. PMIYC engages LLMs in multi-turn conversations, playing roles of both the persuader and the persuadee. It measures not just how well they can convince but also how resistant they're to external influence.

PMIYC's evaluation covers a wide array of models and scenarios, from subjective topics to outright misinformation. This isn't just theoretical. The framework's results align with human assessments from earlier studies, providing a credible benchmark for persuasion in AI.

LLMs in the Persuasion Arena

So, who's leading the charge? Llama-3.3-70B and GPT-4o emerge as frontrunners, demonstrating comparable persuasive prowess. They outperform Claude 3 Haiku by a notable 30%. However, a twist in the tale, GPT-4o boasts over 50% greater resistance to persuasion by misinformation compared to Llama-3.3-70B. o4-mini takes the spotlight as well, showing strong performance as a persuader and a resistant persuadee.

What does this mean for AI safety? These insights are important for designing systems that not only persuade but do so ethically. They could pave the way for more reliable systems that aren't easily swayed by malicious intent.

Why It Matters

Here's the real question: as LLMs grow more powerful, can we trust them to hold the line on ethical standards? The evidence suggests both promise and peril. Developers and researchers should consider the implications seriously. A framework like PMIYC is essential, offering empirical data that could inform safer AI practices.

In a world where AI's influence is set to expand, understanding how these models interact and what makes them tick isn't just academic. It's a necessary step toward harnessing AI's potential responsibly. The paper's key contribution is in providing a model to assess and understand this complex landscape.