Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics
This research highlights a significant risk for society by showing that LLMs can be used to create effective disinformation campaigns, which is an incremental but concerning advancement in AI persuasion capabilities.
The study investigated how personalized arguments and fabricated statistics from LLMs can persuade humans in debates, finding that a mixed strategy increased the chance of changing participants' opinions to 51%, compared to 32% for static human-written arguments.
Large Language Models (LLMs) are becoming increasingly persuasive, demonstrating the ability to personalize arguments in conversation with humans by leveraging their personal data. This may have serious impacts on the scale and effectiveness of disinformation campaigns. We studied the persuasiveness of LLMs in a debate setting by having humans $(n=33)$ engage with LLM-generated arguments intended to change the human's opinion. We quantified the LLM's effect by measuring human agreement with the debate's hypothesis pre- and post-debate and analyzing both the magnitude of opinion change, as well as the likelihood of an update in the LLM's direction. We compare persuasiveness across established persuasion strategies, including personalized arguments informed by user demographics and personality, appeal to fabricated statistics, and a mixed strategy utilizing both personalized arguments and fabricated statistics. We found that static arguments generated by humans and GPT-4o-mini have comparable persuasive power. However, the LLM outperformed static human-written arguments when leveraging the mixed strategy in an interactive debate setting. This approach had a $\mathbf{51\%}$ chance of persuading participants to modify their initial position, compared to $\mathbf{32\%}$ for the static human-written arguments. Our results highlight the concerning potential for LLMs to enable inexpensive and persuasive large-scale disinformation campaigns.