Summary of Experiments in News Bias Detection with Pre-trained Neural Transformers, by Tim Menzner et al.
Experiments in News Bias Detection with Pre-Trained Neural Transformers
by Tim Menzner, Jochen L. Leidner
First submitted to arxiv on: 14 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 the effectiveness of various large-scale language models in identifying biased or false news reports. The researchers train and evaluate multiple models on a specific task: detecting the level of bias in individual sentences from news articles, as well as categorizing different types of biases. By analyzing the performance of each model, this study aims to provide insights into which approaches are most suitable for addressing the spread of misinformation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to find biased or fake news reports online. It compares many big language models to see which one is best at figuring out if a sentence from a news article is true or not. The researchers trained and tested these models to classify bias levels in sentences and identify different types of biases. |
