The paper explores different pre-training methods for adapting language models to the healthcare domain, with a focus on improving the quality of document-level embeddings for downstream tasks. Three pre-training approaches are assessed:
The models are evaluated on downstream document classification tasks for three healthcare datasets: MIMIC-III, Oxford Health Foundation Trust (OHFT), and NHS Patient Safety Incident Reports (PSIR). The results show that contrastively trained models outperform other approaches on the classification tasks, delivering strong performance from limited labeled data and with fewer model parameter updates required. While metadata-based pre-training does not further improve classifications across the datasets, it yields interesting embedding cluster separability.
The paper highlights the importance of domain-specialization for language models, and provides pre-training guidelines for developing compact healthcare-focused language models that can be efficiently deployed in resource-constrained local healthcare settings. It also motivates continued inquiry into contrastive pre-training objectives and demonstrates adaptation techniques to align small language models with privacy-sensitive medical tasks.
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arxiv.org
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by Niall Taylor... ב- arxiv.org 04-01-2024
https://arxiv.org/pdf/2403.19802.pdfשאלות מעמיקות