Papers with BASIL
The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection (2025.findings-naacl)
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Tomáš Horych, Christoph Mandl, Terry Ruas, Andre Greiner-Petter, Bela Gipp, Akiko Aizawa, Timo Spinde
| Challenge: | Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. |
| Approach: | They propose to use Large Language Models to automate annotation process and train classifiers on large datasets. |
| Outcome: | The proposed model outperforms all of the annotator LLMs on two media bias benchmark datasets (BABE and BASIL) while maintaining data quality. |
In Plain Sight: Media Bias Through the Lens of Factual Reporting (D19-1)
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| Challenge: | lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization. |
| Approach: | They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias. |
| Outcome: | The proposed model shows that informational bias appears more frequently than lexical bias. |
Large Language Models as Reader for Bias Detection (2025.findings-emnlp)
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| Challenge: | Traditional methods analyze text from the writer’s perspective, leaving the reader’s viewpoint underexplored. |
| Approach: | They investigate whether large language models can be leveraged as readers for bias detection by generating reader-perspective comments. |
| Outcome: | The proposed model performs comparable to GPT4's in detecting bias in media content. |