LLM-impersonated debate contributions are more authentic, relevant and coherent than their original: A representative study using BBC1’s Question Time - Steffen Herbold; Alexander Trautsch; Zlata Kikteva; Annette Hautli-Janisz (PLoS ONE, 2026)
Generative AI has the potential to pollute the public information sphere with made-up content, posing a significant threat to the cohesion of societies at large. This paper offers the first large-scale and systematic study of how authentic, relevant and coherent impersonated content from Large Language Models (LLMs) is perceived by the general public. Based on a cross-section of British society, we show that LLM-generated responses to questions drawn from a broadcast political debate programme in the UK are judged to be more authentic and relevant than the original responses given by the panel members who were impersonated. We also show that stylistic differences do not influence these judgments, meaning that the distinction of original and generated content is challenging for the general public. Taken together, this means that LLMs can be made to deceive the public regarding the nature of statements in the political domain, with the consequence that there is a dire need to inform the general public of the potential harm this can have on society.