Summary of Megafake: a Theory-driven Dataset Of Fake News Generated by Large Language Models, By Lionel Z. Wang et al.
MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models
by Lionel Z. Wang, Yiming Ma, Renfei Gao, Beichen Guo, Han Zhu, Wenqi Fan, Zexin Lu, Ka Chung Ng
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The advent of large language models (LLMs) has transformed online content creation, making it easier to generate high-quality fake news. This misuse threatens digital integrity and ethical standards. Understanding motivations and mechanisms behind LLM-generated fake news is crucial. The study develops a comprehensive LLM-based theoretical framework, LLM-Fake Theory, analyzing the creation of fake news from a social psychology perspective. A novel pipeline automates fake news generation using LLMs, eliminating manual annotation needs. The pipeline creates a theoretically informed Machine-generated Fake news dataset, MegaFake, derived from the GossipCop dataset. Comprehensive analyses evaluate the MegaFake dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made it easy to create fake news online. This is bad because it makes it hard to trust what we read on the internet. To understand why this is happening, scientists studied how large language models work and developed a new way to think about it called LLM-Fake Theory. They created a machine that can generate fake news using these models, which helps us study and solve this problem better. The machine made a big dataset of fake news, which they tested to see if it’s good enough for research. |
