Papers by Timo Spinde
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. |
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts (2021.findings-emnlp)
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| Challenge: | Existing studies on the detection and aggregation of media bias lack a gold standard data set and high context dependencies. |
| Approach: | They propose to use a data set to identify media bias by word and sentence level . they propose to train a model to detect bias-inducing sentences in news articles automatically . |
| Outcome: | The proposed model outperforms existing methods on a large corpus of labels on the word and sentence level. |
MAGPIE: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions (2024.lrec-main)
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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. |