Summary of Real-time Fake News From Adversarial Feedback, by Sanxing Chen et al.
Real-time Fake News from Adversarial Feedback
by Sanxing Chen, Yukun Huang, Bhuwan Dhingra
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 abstract discusses the limitations of existing evaluations for fake news detection using conventional sources. Despite high accuracies reported by Large Language Model (LLM)-based detectors, recent popular fake news from such sources can be easily detected due to pre-training and retrieval corpus contamination or shallow patterns. The authors argue that a proper fake news detection dataset should test a model’s ability to reason factually about the current world by retrieving and reading related evidence. To achieve this, they develop a novel pipeline that leverages natural language feedback from a Retrieval-Augmented Generator (RAG)-based detector to iteratively modify real-time news into deceptive fake news challenging LLMs. The results show an absolute 17.5% decrease in binary classification ROC-AUC for a strong RAG-based GPT-4o detector, highlighting the role of RAG in both detecting and generating fake news. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news detection models often claim to be highly accurate, but this might not be entirely true. The researchers looked into how well these models work using real-world data from fact-checking websites. They found that even though these models are good at detecting certain types of fake news, they still have a lot of trouble with new and unexpected information. To make things more challenging for these models, the authors created a new way to generate fake news that’s harder to detect. They did this by having a model look at real news and then modify it to make it sound like fake news. This approach showed that even strong models can struggle with detecting certain types of fake news. |
Keywords
» Artificial intelligence » Auc » Classification » Gpt » Large language model » Rag
