# Disinformation Capabilities of Large Language Models

arXiv: https://arxiv.org/abs/2311.08838 · HTML: https://mlsanity.com/paper/2311.08838/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Low-Resource Languages Jailbreak GPT-4](https://mlsanity.com/paper/2310.02446/) (2023)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://mlsanity.com/paper/2212.12017/) (2022)
- [Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs](https://mlsanity.com/paper/2308.13387/) (2023)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [TweepFake: about Detecting Deepfake Tweets](https://mlsanity.com/paper/2008.00036/) (2020)
- [Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection](https://mlsanity.com/paper/1907.09177/) (2019)
- [Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wording](https://mlsanity.com/paper/2306.06199/) (2023)

## Built on by (most cited first, 25 of 25)

- [Source Code Summarization in the Era of Large Language Models](https://mlsanity.com/paper/2407.07959/) (2024)
- [Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges](https://mlsanity.com/paper/2403.18249/) (2024)
- [Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2508.20697/) (2025)
- [MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models](https://mlsanity.com/paper/2408.11871/) (2024)
- [FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter](https://mlsanity.com/paper/2508.19639/) (2025)
- [PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks](https://mlsanity.com/paper/2605.10977/) (2026)
- [mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection](https://mlsanity.com/paper/2506.01702/) (2025)
- [A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models](https://mlsanity.com/paper/2410.21360/) (2024)
- [Information Retrieval in the Age of Generative AI: The RGB Model](https://mlsanity.com/paper/2504.20610/) (2025)
- [Tailored untruths: How personalisation challenges LLM safeguards](https://mlsanity.com/paper/2510.12993/) (2025)
- [Beyond Raw Detection Scores: Markov-Informed Calibration for Boosting Machine-Generated Text Detection](https://mlsanity.com/paper/2602.08031/) (2026)
- [BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages](https://mlsanity.com/paper/2603.00634/) (2026)
- [Retrieving Climate Change Disinformation by Narrative](https://mlsanity.com/paper/2603.22015/) (2026)
- [EAMET: Robust Massive Model Editing via Embedding Alignment Optimization](https://mlsanity.com/paper/2505.11876/) (2025)
- [Jailbreaking LLMs Without Gradients or Priors: Effective and Transferable Attacks](https://mlsanity.com/paper/2601.03420/) (2026)
- [A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models](https://mlsanity.com/paper/2503.15205/) (2025)
- [WaterSearch: Exploring Seed Pooling for Improving the Quality-Detectability Trade-off in LLM Watermarking](https://mlsanity.com/paper/2512.00837/) (2025)
- [Ignore All Previous Instructions: Jailbreaking as a de-escalatory peace building practise to resist LLM social media bots](https://mlsanity.com/paper/2603.01942/) (2026)
- [From Shallow to Deep: Pinning Semantic Intent via Causal GRPO](https://mlsanity.com/paper/2603.02675/) (2026)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)
- [Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection](https://mlsanity.com/paper/2605.16107/) (2026)
- [Using LLMs to Detect LLM-Generated Texts: A Cross-Generation Analysis](https://mlsanity.com/paper/2609.34691/) (2026)
- [VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation](https://mlsanity.com/paper/2609.35028/) (2026)
- [CrisisFake: Benchmark Validity of AI-Generated Text Detection for Disaster Social Sensing](https://mlsanity.com/paper/2609.35821/) (2026)
- [Misinformation Without Triggers: From Factual Answers to Downstream Decisions](https://mlsanity.com/paper/2610.02886/) (2026)

## Abstract (arXiv)

Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for societies around the world. This paper presents a comprehensive study of the disinformation capabilities of the current generation of LLMs to generate false news articles in the English language. In our study, we evaluated the capabilities of 10 LLMs using 20 disinformation narratives. We evaluated several aspects of the LLMs: how good they are at generating news articles, how strongly they tend to agree or disagree with the disinformation narratives, how often they generate safety warnings, etc. We also evaluated the abilities of detection models to detect these articles as LLM-generated. We conclude that LLMs are able to generate convincing news articles that agree with dangerous disinformation narratives.