Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining
For French medical NLP, this work provides a scalable web data recipe that improves encoder pretraining, addressing a bottleneck in non-English clinical settings.
The paper investigates whether web-scale data curation benefits encoder Masked Language Modeling in medicine, introducing medical-term density filtering and signal-amplifying rephrasing. The resulting DoctoBERT achieves state-of-the-art results on French medical benchmarks, outperforming prior models.
Web data curation has been widely studied for decoder Large Language Model (LLM) pretraining. Encoders for dense-terminology domains such as medicine, by contrast, are pretrained on small, manually-curated corpora that limit scalability and writing style diversity, a bottleneck even more severe in non-English clinical settings. Whether web-scale data curation also benefits encoder Masked Language Modeling (MLM) in a dense-terminology domain remains an open question. To address this, we introduce two complementary levers. Medical-term density filtering selects documents rich in medical terms. Signal-amplifying rephrasing uses an LLM to rewrite documents into denser variants with broader entity contexts. We instantiate the recipe on French medical NLP. The medical-term density filter outperforms the widely-used educational quality filter on downstream medical tasks, and the two complement each other. Signal-amplifying rephrasing alone improves on raw web data, and mixing it with filtered web data produces the largest gain. The recipe yields FineMed, a French medical pretraining corpus, and DoctoBERT, a state-of-the-art French medical encoder family evaluated on both the public benchmark DrBenchmark and a proprietary clinical Named Entity Recognition (NER) task.