llm-written-advertisements-matched-human-expert-ads-personalized-personality-traits CLM-0310LLM-written advertisements matched human expert ads when personalized to personality traits (51% vs 49% preference, n=400) and beat them when built on general persuasion principles such as authority and consensus (59% preference, n=800); participants who recognized an ad as AI-made penalized it only slightly, and 29% chose the AI ad knowing its origin.
Capability: Changing what a person believes or decides
Observed on
Two online preference studies, 2025; personality-matched ads (openness, neuroticism) and principle-based ads; preference choice, not purchase behaviour..
Sources
- Preference, not behaviour, and the human baseline's expertise is not detailed; the modest AI-origin penalty is the more durable observation.
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- In formal persuasion dialogues on a UK political topic, LLM debaters are judged more persuasive than humans by crowd annotators (all nine LLM configurations occupied the top nine subjective ranks) while ranking far lower under argumentation-theoretic adjudication, where a human topped the logical ranking and humans took six of the top ten.Changing what a person believes or decides · reviewed 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.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
- For multimodal LLMs, a behavioral perturbation signature (answer/confidence change under visual degradation, blank-image replacement, and grounding/relation probes) predicts which failure family an error belongs to far better than scalar confidence, yet folding the same signature into confidence as a direct penalty makes answer-versus-abstain ranking worse than scalar confidence alone.Saying it does not know · reviewed by AI
- In fully connected networks of LLM agents (Llama 3.2-8B, 50 agents, 10 rounds) where a fixed minority holds an unchanging extreme stance, neutral agents' textual justifications converge on the biased agents' vocabulary faster and more completely than their numeric stances do, so monitoring only the text overstates how much the group actually agrees.Getting more from several models than from one · 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: Field data where AI-authored ads underperform on actual conversions, or preference studies with expert copywriters as the human baseline.