# ChatGPT for President! Presupposed content in politicians versus GPT-generated texts

arXiv: https://arxiv.org/abs/2503.01269 · HTML: https://mlsanity.com/paper/2503.01269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 65 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [The Ghost in the Machine has an American accent: value conflict in GPT-3](https://mlsanity.com/paper/2203.07785/) (2022)
- [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://mlsanity.com/paper/2004.03066/) (2020)
- [A fine-grained comparison of pragmatic language understanding in humans and language models](https://mlsanity.com/paper/2212.06801/) (2022)
- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities](https://mlsanity.com/paper/2401.07078/) (2024)
- [The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs](https://mlsanity.com/paper/2210.14986/) (2022)
- [Artificial Influence: An Analysis Of AI-Driven Persuasion](https://mlsanity.com/paper/2303.08721/) (2023)
- [Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom](https://mlsanity.com/paper/2404.19509/) (2024)
- [In Generative AI we Trust: Can Chatbots Effectively Verify Political Information?](https://mlsanity.com/paper/2312.13096/) (2023)
- [Shall androids dream of genocides? How generative AI can change the future of memorialization of mass atrocities](https://mlsanity.com/paper/2305.14358/) (2023)
- [ChatGPT as speechwriter for the French presidents](https://mlsanity.com/paper/2411.18382/) (2024)
- [Politicians vs ChatGPT. A study of presuppositions in French and Italian political communication](https://mlsanity.com/paper/2411.18403/) (2024)

## Abstract (arXiv)

This study examines ChatGPT-4's capability to replicate linguistic strategies used in political discourse, focusing on its potential for manipulative language generation. As large language models become increasingly popular for text generation, concerns have grown regarding their role in spreading fake news and propaganda. This research compares real political speeches with those generated by ChatGPT, emphasizing presuppositions (a rhetorical device that subtly influences audiences by packaging some content as already known at the moment of utterance, thus swaying opinions without explicit argumentation). Using a corpus-based pragmatic analysis, this study assesses how well ChatGPT can mimic these persuasive strategies. The findings reveal that although ChatGPT-generated texts contain many manipulative presuppositions, key differences emerge in their frequency, form, and function compared with those of politicians. For instance, ChatGPT often relies on change-of-state verbs used in fixed phrases, whereas politicians use presupposition triggers in more varied and creative ways. Such differences, however, are challenging to detect with the naked eye, underscoring the potential risks posed by large language models in political and public discourse.Using a corpus-based pragmatic analysis, this study assesses how well ChatGPT can mimic these persuasive strategies. The findings reveal that although ChatGPT-generated texts contain many manipulative presuppositions, key differences emerge in their frequency, form, and function compared with those of politicians. For instance, ChatGPT often relies on change-of-state verbs used in fixed phrases, whereas politicians use presupposition triggers in more varied and creative ways. Such differences, however, are challenging to detect with the naked eye, underscoring the potential risks posed by large language models in political and public discourse.