Summary of Fact or Fiction? Can Llms Be Reliable Annotators For Political Truths?, by Veronica Chatrath et al.
Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths?
by Veronica Chatrath, Marcelo Lotif, Shaina Raza
First submitted to arxiv on: 8 Nov 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 study explores the potential of large language models (LLMs) as reliable annotators for detecting political factuality in news articles. Researchers created a diverse dataset using open-source LLMs, labelling it for bias through annotations generated by these models. Human experts validated these annotations, which were then evaluated by LLM-based judges to assess accuracy and reliability. This approach offers a scalable and robust alternative to traditional fact-checking methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language computers can help us find true information in news articles. Right now, people have to read through lots of news to find the truth, but this is hard work and can be biased. The researchers used these big language computers to make a special list of news articles that are either true or not true. They got smart people to check if the computer’s answers were correct, and then they used another computer program to see how well it did. This new way of doing things could help us find the truth faster and more easily. |
