Summary of Exploring the Potential Of the Large Language Models (llms) in Identifying Misleading News Headlines, by Md Main Uddin Rony et al.
Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines
by Md Main Uddin Rony, Md Mahfuzul Haque, Mohammad Ali, Ahmed Shatil Alam, Naeemul Hassan
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 research explores the effectiveness of Large Language Models (LLMs) in identifying misleading news headlines. The study utilizes a dataset of 60 articles from reputable and questionable outlets across various domains, and employs three LLMs: ChatGPT-3.5, ChatGPT-4, and Gemini, for classification. The results show significant variance in model performance, with ChatGPT-4 demonstrating superior accuracy in cases with unanimous annotator agreement on misleading headlines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses big language models to check if news headlines are true or false. They looked at 60 articles from different places that were good and not so good, and used three of these models: ChatGPT-3.5, ChatGPT-4, and Gemini. The models did a pretty good job, but one model was way better than the others at finding fake headlines. |
Keywords
» Artificial intelligence » Classification » Gemini
