@InProceedings{Kettler2024_1217,
<br/>   author = {Benedict Kettler and Stefan Hillmann},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2024},
<br/>   title = {Supervised vs. Zero-Shot Learning Automatic Classification of Comments on Educational Videos Using Pre-Trained Language Models},
<br/>   year = {2024},
<br/>   editor = {Timo Baumann},
<br/>   month = mar,
<br/>   pages = {149--156},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {Despite the potential of AI, only a small percentage of small and
medium-sized enterprises (SMEs) are adopting it due to data issues, expertise gaps,
and implementation barriers. Zero-shot learning offers a promising approach for
SMEs by minimizing these obstacles. This paper explores the use of zero-shot
learning in a real-world NLP classification task on online comments (comparable
with intent classification tasks) from the e-learning platform Sofatutor. While finetuning
has achieved high accuracy (82.3–86.5%), zero-shot models have shown
lower performance (39.3–61.4%) due to different label selection, grouping of different
scenarios in one class and the type of classification task. Even if the current
accuracy is not sufficient for practical application, pre-filtering the data using zeroshot
learning might be a promising option for SMEs.},
<br/>   isbn = {978-3-95908-325-6},
<br/>   issn = {0940-6832},
<br/>   keywords = {Large Language Models},
<br/>   url = {https://www.essv.de/pdf/2024_149_156.pdf},
<br/>   doi = {10.35096/othr/pub-7092},
<br/>}