# From Deception to Detection: The Dual Roles of Large Language Models in Fake News

arXiv: https://arxiv.org/abs/2409.17416 · HTML: https://mlsanity.com/paper/2409.17416/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 25 Sep 2024.

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

- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Multi-step Jailbreaking Privacy Attacks on ChatGPT](https://mlsanity.com/paper/2304.05197/) (2023)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [On the Possibilities of AI-Generated Text Detection](https://mlsanity.com/paper/2304.04736/) (2023)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [SlimPajama-DC: Understanding Data Combinations for LLM Training](https://mlsanity.com/paper/2309.10818/) (2023)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions](https://mlsanity.com/paper/2107.07015/) (2021)
- [Adapting Fake News Detection to the Era of Large Language Models](https://mlsanity.com/paper/2311.04917/) (2023)
- [Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT](https://mlsanity.com/paper/2306.07401/) (2023)
- [Med-MMHL: A Multi-Modal Dataset for Detecting Human- and LLM-Generated Misinformation in the Medical Domain](https://mlsanity.com/paper/2306.08871/) (2023)

## Built on by (most cited first, 11 of 11)

- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems](https://mlsanity.com/paper/2601.21963/) (2026)
- [ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs](https://mlsanity.com/paper/2506.01386/) (2025)
- [CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing](https://mlsanity.com/paper/2603.19297/) (2026)
- [FNDEX: Fake News and Doxxing Detection with Explainable AI](https://mlsanity.com/paper/2410.22390/) (2024)
- [Leveraging Knowledge Graphs and LLMs for Structured Generation of Misinformation](https://mlsanity.com/paper/2505.24479/) (2025)
- [ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation](https://mlsanity.com/paper/2601.12983/) (2026)
- [Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse](https://mlsanity.com/paper/2605.21540/) (2026)
- [Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability](https://mlsanity.com/paper/2607.04061/) (2026)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)
- [From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake News](https://mlsanity.com/paper/2609.20838/) (2026)

## Abstract (arXiv)

Fake news poses a significant threat to the integrity of information ecosystems and public trust. The advent of Large Language Models (LLMs) holds considerable promise for transforming the battle against fake news. Generally, LLMs represent a double-edged sword in this struggle. One major concern is that LLMs can be readily used to craft and disseminate misleading information on a large scale. This raises the pressing questions: Can LLMs easily generate biased fake news? Do all LLMs have this capability? Conversely, LLMs offer valuable prospects for countering fake news, thanks to their extensive knowledge of the world and robust reasoning capabilities. This leads to other critical inquiries: Can we use LLMs to detect fake news, and do they outperform typical detection models? In this paper, we aim to address these pivotal questions by exploring the performance of various LLMs. Our objective is to explore the capability of various LLMs in effectively combating fake news, marking this as the first investigation to analyze seven such models. Our results reveal that while some models adhere strictly to safety protocols, refusing to generate biased or misleading content, other models can readily produce fake news across a spectrum of biases. Additionally, our results show that larger models generally exhibit superior detection abilities and that LLM-generated fake news are less likely to be detected than human-written ones. Finally, our findings demonstrate that users can benefit from LLM-generated explanations in identifying fake news.