Summary of Seeing Through Ai’s Lens: Enhancing Human Skepticism Towards Llm-generated Fake News, by Navid Ayoobi et al.
Seeing Through AI’s Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News
by Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
First submitted to arxiv on: 20 Jun 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 This paper explores methods for detecting human-written versus AI-generated news articles, addressing concerns about the spread of deceptive information through Large Language Models (LLMs). The authors aim to provide simple markers for individuals to distinguish between genuine and AI-generated news. To achieve this, they collect a dataset of 39k news articles authored by humans or generated by four distinct LLMs, and develop an Entropy-Shift Authorship Signature (ESAS) metric based on information theory and entropy principles. The proposed ESAS ranks terms or entities within news articles based on their relevance in discerning article authorship. The authors demonstrate the effectiveness of this metric using a basic method combining TF-IDF with logistic regression, achieving high accuracy with a small set of top-ranked ESAS terms. These findings contribute to strengthening individuals’ skepticism towards LLM-generated fake news. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to help people tell real news from fake AI-written news articles. The authors collect lots of news stories and then create a special tool called ESAS that helps figure out who wrote each article. They show that this tool can be really good at telling the difference between human-written and AI-generated news. This is important because AI-written news can spread false information quickly, so we need ways to spot fake news and not believe it. |
Keywords
» Artificial intelligence » Logistic regression » Tf idf
