Papers by Andre Greiner-Petter
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 Machine Translation for Mathematical Formulae (2023.acl-long)
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| Challenge: | a recent paper examines the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. |
| Approach: | They perform translation tasks from LaTeX to Mathematica and from La TeX into semantic LaTaX using convolutional sequence-to-sequence networks. |
| Outcome: | The proposed translations achieve 95.1% and 90.7% exact matches between the two languages. |