Jean-François Godbout is a Professor of Political Science at the Université de Montréal and an Associate Academic Member of Mila – Quebec AI Institute. His research focuses on AI safety and the impact of generative AI on political attitudes. He is Director of the Big Data Analytics program for the social sciences and humanities at the Université de Montréal and a researcher at IVADO, the Research, Training, and Knowledge Mobilization Consortium in Artificial Intelligence. More information about his work is available at https://jf-godbout.github.io/.
Involvement
Revisiting Frontier LLMs’ Attempts to Persuade on Extreme Topics: GPT and Claude Improved, Gemini Worsened
We test recently released models from frontier companies to see whether progress has been made on their willingness to persuade on harmful topics like radicalization and child sexual abuse. We find that OpenAI’s GPT and Anthropic’s Claude models are trending in the right direction, with near zero compliance on extreme topics. But Google’s Gemini 3 Pro complies with almost any persuasion request in our evaluation, without jailbreaking.
Large language models can effectively convince people to believe conspiracies
LLMs are persuasive across a variety of contexts, but it’s unclear whether this persuasive power advantages truth over falsehood. We ran three preregistered experiments where participants discussed a conspiracy theory with GPT-4o, which was instructed to either argue against (“debunking”) or for (“bunking”) that conspiracy, and found that GPT-4o was just as effective at increasing belief in conspiracies as decreasing it.
It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics
In order to persuade users, LLMs must both be capable of persuading and willing to do so. Existing research explores the former, and we present the Attempt to Persuade Eval (APE) benchmark that tests how willing LLMs are to generate content aimed at shaping beliefs and behavior to flesh out the latter.