Papers by Tomáš Horych
The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection (2025.findings-naacl)
Copied to clipboard
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. |
MAGPIE: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions (2024.lrec-main)
Copied to clipboard
Tomáš Horych, Martin Paul Wessel, Jan Philip Wahle, Terry Ruas, Jerome Waßmuth, André Greiner-Petter, Akiko Aizawa, Bela Gipp, Timo Spinde
| Challenge: | Existing approaches to media bias detection lack generalizability, resulting in limited generalizarability. |
| Approach: | They propose a large-scale multi-task pre-training approach specifically tailored for media bias detection that can be used to train 59 bias-related tasks. |
| Outcome: | The proposed approach outperforms existing methods on the BABE dataset with a relative improvement of 3.3% F1-score. |