ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language

• URL: http://arxiv.org/abs/2601.09722v1
• Date: Sat, 27 Dec 2025 10:00:52 GMT
• Title: ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language
• Authors: Franciszek Górski, Andrzej Czyżewski,
• Abstract summary: This work is part of a larger project called ADMEDVOICE.<n>We collected an extensive corpus of medical texts representing five clinical categories.<n>We used the multilingual Llama3.1 model to annotate this corpus.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we present an annotation framework that demonstrates how a multilingual LLM pretrained on a large corpus can be used as a teacher model to distill the expert knowledge needed for tagging medical texts in Polish. This work is part of a larger project called ADMEDVOICE, within which we collected an extensive corpus of medical texts representing five clinical categories - Radiology, Oncology, Cardiology, Hypertension, and Pathology. Using this data, we had to develop a multi-class classifier, but the fundamental problem turned out to be the lack of resources for annotating an adequate number of texts. Therefore, in our solution, we used the multilingual Llama3.1 model to annotate an extensive corpus of medical texts in Polish. Using our limited annotation resources, we verified only a portion of these labels, creating a test set from them. The data annotated in this way were then used for training and validation of 3 different types of classifiers based on the BERT architecture - the distilled DistilBERT model, BioBERT fine-tuned on medical data, and HerBERT fine-tuned on the Polish language corpus. Among the models we trained, the DistilBERT model achieved the best results, reaching an F1 score > 0.80 for each clinical category and an F1 score > 0.93 for 3 of them. In this way, we obtained a series of highly effective classifiers that represent an alternative to large language models, due to their nearly 500 times smaller size, 300 times lower GPU VRAM consumption, and several hundred times faster inference.

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