Summary of Comprehensive Study on German Language Models For Clinical and Biomedical Text Understanding, by Ahmad Idrissi-yaghir et al.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
by Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer, Kamyar Arzideh, Giulia Baldini, Jan Trienes, Max Hasin, Jeanette Bewersdorff, Cynthia S. Schmidt, Marie Bauer, Kaleb E. Smith, Jiang Bian, Yonghui Wu, Jörg Schlötterer, Torsten Zesch, Peter A. Horn, Christin Seifert, Felix Nensa, Jens Kleesiek, Christoph M. Friedrich
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores strategies for adapting pre-trained language models like BERT and RoBERTa to domain-specific requirements in natural language processing (NLP). This is particularly important in specialized domains like medicine, where unique terminology, abbreviations, and document structures are common. The authors focus on continuous pre-training of German medical language models using public English medical data and German clinical data. They evaluate the resulting models on various German downstream tasks, including named entity recognition, multi-label classification, and extractive question answering. The results show that models augmented by clinical and translation-based pre-training outperform general domain models in medical contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work better for specific areas of medicine. Right now, these models are really good at understanding everyday language, but they can struggle when it comes to specialized medical texts. To solve this problem, the authors train special German medical language models using a mix of English and German medical data. They then test these models on tasks like identifying important words or answering questions. The results show that these specially trained models do better than general language models in medical contexts. |
Keywords
» Artificial intelligence » Bert » Classification » Named entity recognition » Natural language processing » Nlp » Question answering » Translation
