# LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High

arXiv: https://arxiv.org/abs/2505.22354 · HTML: https://mlsanity.com/paper/2505.22354/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 8 of 8)

- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [CREPE: Open-Domain Question Answering with False Presuppositions](https://mlsanity.com/paper/2211.17257/) (2022)
- [(QA)2: Question Answering with Questionable Assumptions](https://mlsanity.com/paper/2212.10003/) (2022)
- [Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering](https://mlsanity.com/paper/2101.00391/) (2021)
- [Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering](https://mlsanity.com/paper/2311.09542/) (2023)
- [Syn-QA2: Evaluating False Assumptions in Long-tail Questions with Synthetic QA Datasets](https://mlsanity.com/paper/2403.12145/) (2024)

## Built on by (most cited first, 12 of 12)

- [Are BabyLMs Deaf to Gricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models](https://mlsanity.com/paper/2510.04764/) (2025)
- [Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations](https://mlsanity.com/paper/2603.29373/) (2026)
- [Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests](https://mlsanity.com/paper/2601.07820/) (2026)
- [Evaluating Evidence Grounding Under User Pressure in Instruction-Tuned Language Models](https://mlsanity.com/paper/2603.20162/) (2026)
- [How Hypocritical Is Your LLM judge? Listener-Speaker Asymmetries in the Pragmatic Competence of Large Language Models](https://mlsanity.com/paper/2604.15873/) (2026)
- [Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs](https://mlsanity.com/paper/2604.19245/) (2026)
- [Evaluating Reasoning Models for Queries with Presuppositions](https://mlsanity.com/paper/2605.03050/) (2026)
- [Assessing Web Search Credibility and Response Groundedness in Chat Assistants](https://mlsanity.com/paper/2510.13749/) (2025)
- [MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication](https://mlsanity.com/paper/2601.09853/) (2026)
- [What Patients Really Ask: Exploring the Effect of False Assumptions in Patient Information Seeking](https://mlsanity.com/paper/2601.15674/) (2026)
- [Retrieval Sensitivity to Identity Signals in Queries](https://mlsanity.com/paper/2609.36534/) (2026)
- [Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively](https://mlsanity.com/paper/2610.17703/) (2026)

## Abstract (arXiv)

This paper examines how LLMs handle false presuppositions and whether certain linguistic factors influence their responses to falsely presupposed content. Presuppositions subtly introduce information as given, making them highly effective at embedding disputable or false information. This raises concerns about whether LLMs, like humans, may fail to detect and correct misleading assumptions introduced as false presuppositions, even when the stakes of misinformation are high. Using a systematic approach based on linguistic presupposition analysis, we investigate the conditions under which LLMs are more or less sensitive to adopt or reject false presuppositions. Focusing on political contexts, we examine how factors like linguistic construction, political party, and scenario probability impact the recognition of false presuppositions. We conduct experiments with a newly created dataset and examine three LLMs: OpenAI's GPT-4-o, Meta's LLama-3-8B, and MistralAI's Mistral-7B-v03. Our results show that the models struggle to recognize false presuppositions, with performance varying by condition. This study highlights that linguistic presupposition analysis is a valuable tool for uncovering the reinforcement of political misinformation in LLM responses.