Summary of Fighting Fire with Fire: Adversarial Prompting to Generate a Misinformation Detection Dataset, by Shrey Satapara et al.
Fighting Fire with Fire: Adversarial Prompting to Generate a Misinformation Detection Dataset
by Shrey Satapara, Parth Mehta, Debasis Ganguly, Sandip Modha
First submitted to arxiv on: 9 Jan 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 This paper proposes an innovative approach to create large-scale datasets for identifying misinformation using large language models (LLMs) like GPT, Bard, and Llama. The authors aim to develop a “silver-standard” ground-truth dataset that can help detect fake news and misinformation. The method involves prompting LLMs to generate summarized versions of trusted news articles with controlled factual errors, such as incorrect quantities or false attributions. This approach could potentially scale well for developing large datasets. To evaluate the effectiveness of this dataset, the authors conduct experiments training various supervised models for misinformation detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big language models to help stop fake news from spreading. Right now, it’s hard to make a huge dataset that shows what misinformation looks like because it takes too much time and effort. The researchers suggest a new way to do this: they use the same language models to generate shorter versions of real news articles with small mistakes in them. This helps create a big dataset quickly. They then test different computer programs to see how well they can spot fake news using this new dataset. |
Keywords
» Artificial intelligence » Gpt » Llama » Prompting » Supervised
