Summary of Measuring Political Bias in Large Language Models: What Is Said and How It Is Said, by Yejin Bang et al.
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
by Yejin Bang, Delong Chen, Nayeon Lee, Pascale Fung
First submitted to arxiv on: 27 Mar 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 proposes a novel approach to measuring political bias in Large Language Models (LLMs), focusing on both content and style of generated text regarding political issues. Existing benchmarks primarily address gender and racial biases, whereas political bias can lead to polarization and harm in downstream applications. The authors advocate for fine-grained and explainable measures to provide transparency to users. They introduce a measure that examines different political topics, such as reproductive rights and climate change, considering both content and style. The proposed framework is scalable to other topics and provides explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how large language models can be biased towards certain political views. Right now, most measures focus on gender or racial biases, but this paper shows that political bias exists too. This bias can cause problems in the future, like making people more divided. To fix this, the authors suggest we need better ways to measure and understand political bias in these models. They propose a new method that looks at both what’s being said (the content) and how it’s being said (the style). The results show that their approach can be used for many different topics and is easy to understand. |
