@inproceedings{idrissi-yaghir-etal-2024-comprehensive,
    title = "Comprehensive Study on {G}erman Language Models for Clinical and Biomedical Text Understanding",
    author = {Idrissi-Yaghir, Ahmad  and
      Dada, Amin  and
      Sch{\"a}fer, Henning  and
      Arzideh, Kamyar  and
      Baldini, Giulia  and
      Trienes, Jan  and
      Hasin, Max  and
      Bewersdorff, Jeanette  and
      Schmidt, Cynthia S.  and
      Bauer, Marie  and
      Smith, Kaleb E.  and
      Bian, Jiang  and
      Wu, Yonghui  and
      Schl{\"o}tterer, J{\"o}rg  and
      Zesch, Torsten  and
      Horn, Peter A.  and
      Seifert, Christin  and
      Nensa, Felix  and
      Kleesiek, Jens  and
      Friedrich, Christoph M.},
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.324",
    pages = "3654--3665",
    abstract = "Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.",
}

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.
Idrissi-Yaghir, Ahmad Dada, Amin Sch{\"a}fer, Henning Arzideh, Kamyar Baldini, Giulia Trienes, Jan Hasin, Max Bewersdorff, Jeanette Schmidt, Cynthia S. Bauer, Marie Smith, Kaleb E. Bian, Jiang Wu, Yonghui Schl{\"o}tterer, J{\"o}rg Zesch, Torsten Horn, Peter A. Seifert, Christin Nensa, Felix Kleesiek, Jens Friedrich, Christoph M.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding