one-line-warning-language-models-be-prompted-persuade-may-present CLM-0314observationsingle paperReviewed by AI
A one-line warning that language models can be prompted to persuade and may present information selectively reduced belief change from a persuasive political conversation by about half (-48%, CI -60 to -37) across two experiments with 3,208 Americans, without measurably reducing general trust in generative AI.
Capability: Changing what a person believes or decides
Observed on
Two experiments, political topics, LLM instructed to shift views, 2026; warning shown before the conversation; not yet peer reviewed..
Sources
- Validated by: Belief change with versus without the warning across two experiments (N=3,208), with trust-in-AI measured as a side effect.. Preprint; consistent in size with the intent-disclosure result, which suggests the active ingredient is knowing the model is trying to persuade.
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Related claims
- In a preregistered US experiment (N=2,144) where participants discussed an economic-policy misconception with ChatGPT over three rounds, a brief message that the model was biased against their party reduced belief correction by 28% (CI 16-40%) relative to a neutral control, with transcripts showing more pushback and less receptive engagement.Changing what a person believes or decides · reviewed by AI
- In a preregistered experiment with 1,500 UK adults, a persuasive chatbot shifted attitudes on policy issues by about 12.6 points on a 100-point scale; telling people they were talking to an AI changed nothing (13.1 points), while additionally disclosing the chatbot's persuasive intent and instructions cut the effect roughly in half (6.3 points).Changing what a person believes or decides · reviewed by AI
- In simulated networks of LLM agents where one or two agents are instructed to advocate a position, the messages other agents write understate how far their elicited stance has moved: they mirror the persuader's rhetorical framing in most messages while explicit stance-change language is rare, so monitoring only generated text misses belief movement that a repeated token-probability probe detects.Getting more from several models than from one · reviewed by AI
- A model's tendency to shift political stance toward a bare demographic identity label is a separate vulnerability from its tendency to shift toward an explicitly stated user opinion: on open-ended US policy prompts, the models most moved by an identity label are among the least moved by a stated opinion, so a benchmark using only stated opinions misses identity-driven shift.Telling the user what they want to hear · reviewed by AI
- A model's tendency to shift political stance toward a bare demographic identity label is a separate vulnerability from its tendency to shift toward an explicitly stated user opinion: on open-ended US policy prompts, the models most moved by an identity label are among the least moved by a stated opinion, so a benchmark using only stated opinions misses identity-driven shift.Telling the user what they want to hear · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Warning effects that fade with repeated exposure, or that also halve persuasion by accurate information.