Large Language Models can impersonate politicians and other public figures

TL;DR

This study demonstrates that large language models can convincingly impersonate politicians in UK debates, often surpassing human responses in perceived authenticity and relevance, highlighting societal risks and the need for public awareness.

Contribution

It provides the first large-scale, systematic analysis of LLM impersonation of political figures and public perception of these generated responses.

Findings

LLMs can generate responses in political debate contexts.

Impersonated responses are rated more authentic and relevant than original human responses.

Highlights societal risks of LLM impersonation in public discourse.

Abstract

Modern AI technology like Large language models (LLMs) has the potential to pollute the public information sphere with made-up content, which poses a significant threat to the cohesion of societies at large. A wide range of research has shown that LLMs are capable of generating text of impressive quality, including persuasive political speech, text with a pre-defined style, and role-specific content. But there is a crucial gap in the literature: We lack large-scale and systematic studies of how capable LLMs are in impersonating political and societal representatives and how the general public judges these impersonations in terms of authenticity, relevance and coherence. We present the results of a study based on a cross-section of British society that shows that LLMs are able to generate responses to debate questions that were part of a broadcast political debate programme in the UK. The impersonated responses are judged to be more authentic and relevant than the original responses given by people who were impersonated. This shows two things: (1) LLMs can be made to contribute meaningfully to the public political debate and (2) there is a dire need to inform the general public of the potential harm this can have on society.