Summary of Quantifying Generative Media Bias with a Corpus Of Real-world and Generated News Articles, by Filip Trhlik and Pontus Stenetorp
Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles
by Filip Trhlik, Pontus Stenetorp
First submitted to arxiv on: 16 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 The paper investigates large language models’ (LLMs) behavior in the field of journalism, specifically focusing on their potential political biases. To achieve this, the researchers created a new dataset consisting of 2,100 human-written articles and used nine LLMs to generate synthetic articles based on these descriptions. The study analyzed shifts in properties between human-authored and machine-generated articles, detecting political bias using both supervised models and LLMs. The findings reveal significant disparities between base and instruction-tuned LLMs, with the latter exhibiting consistent political bias. Moreover, the paper explores how LLMs behave as classifiers, demonstrating their display of political bias even in this role. The study provides a framework and a structured dataset for quantifiable experiments, serving as a foundation for further research into LLM political bias and its implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being used to help with journalism tasks, but we don’t know much about how they work or if they’re biased towards certain political views. The researchers in this study created a big dataset of human-written articles and had nine computer programs (LLMs) generate fake articles based on those descriptions. They looked at how the real and fake articles compared and found that some of the LLMs were biased towards one side of politics or the other, even when they were supposed to be neutral. This study helps us understand how LLMs work in journalism and could help us make better decisions about using them for this purpose. |
Keywords
» Artificial intelligence » Supervised
