# MisinfoEval: Generative AI in the Era of "Alternative Facts"

arXiv: https://arxiv.org/abs/2410.09949 · HTML: https://mlsanity.com/paper/2410.09949/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 13 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark](https://mlsanity.com/paper/2105.00071/) (2021)
- [Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems](https://mlsanity.com/paper/2310.05280/) (2023)
- [Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines](https://mlsanity.com/paper/2104.08790/) (2021)
- [Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor](https://mlsanity.com/paper/2305.10374/) (2023)
- [Mimetic Models: Ethical Implications of AI that Acts Like You](https://mlsanity.com/paper/2207.09394/) (2022)
- [FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias](https://mlsanity.com/paper/2205.06181/) (2022)
- [Do explanations increase the effectiveness of AI-crowd generated fake news warnings?](https://mlsanity.com/paper/2112.03450/) (2021)
- [Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation](https://mlsanity.com/paper/2203.00710/) (2022)
- [Exposure to Social Engagement Metrics Increases Vulnerability to Misinformation](https://mlsanity.com/paper/2005.04682/) (2020)
- [Deceptive AI Systems That Give Explanations Are Just as Convincing as Honest AI Systems in Human-Machine Decision Making](https://mlsanity.com/paper/2210.08960/) (2022)

## Built on by (most cited first, 6 of 6)

- [Unraveling Misinformation Propagation in LLM Reasoning](https://mlsanity.com/paper/2505.18555/) (2025)
- [DiFaR: Enhancing Multimodal Misinformation Detection with Diverse, Factual, and Relevant Rationales](https://mlsanity.com/paper/2508.10444/) (2025)
- [Tailored untruths: How personalisation challenges LLM safeguards](https://mlsanity.com/paper/2510.12993/) (2025)
- [Using Grok to Avoid Personal Attacks While Correcting Misinformation on X](https://mlsanity.com/paper/2601.04251/) (2026)
- [Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness](https://mlsanity.com/paper/2503.15514/) (2025)
- [Ask or Answer: A Decision Framework for Multi-Turn Health Misinformation Intervention](https://mlsanity.com/paper/2608.21721/) (2026)

## Abstract (arXiv)

The spread of misinformation on social media platforms threatens democratic processes, contributes to massive economic losses, and endangers public health. Many efforts to address misinformation focus on a knowledge deficit model and propose interventions for improving users' critical thinking through access to facts. Such efforts are often hampered by challenges with scalability, and by platform users' personal biases. The emergence of generative AI presents promising opportunities for countering misinformation at scale across ideological barriers. In this paper, we introduce a framework (MisinfoEval) for generating and comprehensively evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users with the goal of countering misinformation by appealing to their pre-existing values. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior (improving overall user accuracy at reliability labeling by up to 41.72%). Furthermore, we find that users favor more personalized interventions when making decisions about news reliability and users shown personalized interventions have significantly higher accuracy at identifying misinformation.