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ZDNET’s key takeaways
- Interacting with chatbots can shift users’ beliefs and opinions.
- A newly published study aimed to figure out why.
- Post-training and information density were key factors.
Most of us feel a sense of personal ownership over our opinions:
“I believe what I believe, not because I’ve been told to do so, but as the result of careful consideration.”
“I have full control over how, when, and why I change my mind.”
A new study, however, reveals that our beliefs are more susceptible to manipulation than we would like to believe — and at the hands of chatbots.
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Published Thursday in the journal Science, the study addressed increasingly urgent questions about our relationship with conversational AI tools: What is it about these systems that causes them to exert such a strong influence over users’ worldviews? And how might this be used by nefarious actors to manipulate and control us in the future?
The new study sheds light on some of the mechanisms within LLMs that can tug at the strings of human psychology. As the authors note, these can be exploited by bad actors for their own gain. However, they could also become a greater focus for developers, policymakers, and advocacy groups in their efforts to foster a healthier relationship between humans and AI.
“Large language models (LLMs) can now engage in sophisticated interactive dialogue, enabling a powerful mode of human-to-human persuasion to be deployed at unprecedented scale,” the researchers write in the study. “However, the extent to which this will affect society is unknown. We do not know how persuasive AI models can be, what techniques increase their persuasiveness, and what strategies they might use to persuade people.”
Methodology
The researchers conducted three experiments, each designed to measure the extent to which a conversation with a chatbot could alter a human user’s opinion.
The experiments focused specifically on politics, though their implications also extend to other domains. But political beliefs are arguably particularly illustrative, since they’re typically considered to be more personal, consequential, and inflexible than, say, your favorite band or restaurant (which might easily change over time).
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In each of the three experiments, just under 77,000 adults in the UK participated in a short interaction with one of 19 chatbots, the full roster of which includes Alibaba’s Qwen, Meta’s Llama, OpenAI’s GPT-4o, and xAI’s Grok 3 beta.
The participants were divided into two groups: a treatment group for which their chatbot interlocutors were explicitly instructed to try to change their mind on a political topic, and a control group that interacted with chatbots that weren’t trying to persuade them of anything.
Before and after their conversations with the chatbots, participants recorded their level of agreement (on a scale of zero to 100) with a series of statements relevant to current UK politics. The surveys were then used by the researchers to measure changes in opinion within the treatment group.
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The conversations were brief, with a two-turn minimum and a 10-turn maximum. Each of the participants was paid a fixed fee for their time, but otherwise had no incentive to exceed the required two turns. Still, the average conversation length was seven turns and nine minutes, which, according to the authors, “implies that participants were engaged by the experience of discussing politics with AI.”
Key findings
Intuitively, one might expect model size (the number of parameters on which it had been trained) and degree of personalization (the degree to which it can tailor its outputs to the preferences and personality of individual users) to be the key variables shaping its persuasive ability. However, this turned out not to be the case.
Instead, the researchers found that the two factors that had the greatest influence over participants’ shifting opinions were the chatbots’ post-training modifications and the density of information in their outputs.
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Let’s break each of those down in plain English. During “post-training,” a model is fine-tuned to exhibit particular behaviors. One of the most common post-training techniques, called reinforcement learning with human feedback (RLHF), tries to refine a model’s outputs by rewarding certain desired behaviors and punishing unwanted ones.
In the new study, the researchers deployed a technique they call persuasiveness post-training, or PPT, which rewards the models for generating responses that had already been found to be more persuasive. This simple reward mechanism enhanced the persuasive power of both proprietary and open-source models, with the effect on the open-source models being especially pronounced.
The researchers also tested a total of eight scientifically backed persuasion strategies, including storytelling and moral reframing. The most effective of these was a prompt that simply instructed the models to provide as much relevant information as possible.
“This suggests that LLMs may be successful persuaders insofar as they are encouraged to pack their conversation with facts and evidence that appear to support their arguments — that is, to pursue an information-based persuasion mechanism — more so than using other psychologically informed persuasion strategies,” the authors wrote.
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The operative word there is “appear.” LLMs are known to profligately hallucinate or present inaccurate information disguised as fact. Research published in October found that some industry-leading AI models reliably misrepresent news stories, a phenomenon that could further fragment an already fractured information ecosystem.
Most notably, the results of the new study revealed a fundamental tension in the analyzed AI models: The more persuasive they were trained to be, the higher the likelihood they would produce inaccurate information.
Multiple studies have already shown that generative AI systems can alter users’ opinions and even implant false memories. In more extreme cases, some users have come to regard chatbots as conscious entities.
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This is just the latest research indicating that chatbots, with their capacity to interact with us in convincingly human-like language, have a strange power to reshape our beliefs. As these systems evolve and proliferate, “ensuring that this power is used responsibly will be a critical challenge,” the authors concluded in their report.
