Summary of Adaptation Of Biomedical and Clinical Pretrained Models to French Long Documents: a Comparative Study, by Adrien Bazoge et al.
Adaptation of Biomedical and Clinical Pretrained Models to French Long Documents: A Comparative Study
by Adrien Bazoge, Emmanuel Morin, Beatrice Daille, Pierre-Antoine Gourraud
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores three strategies to adapt language models for processing longer sequences in the French biomedical domain. Building on the success of BERT-based models, researchers investigated using the Longformer architecture to overcome the 512-token input constraint. A comparative study was conducted across 16 downstream tasks, demonstrating that further pre-training an English clinical model with French biomedical texts outperformed other approaches. The findings highlight the benefits of long-sequence models for most tasks, but also emphasize the efficiency of BERT-based models for named entity recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists tried to solve a problem in using language models for medical text analysis. Right now, these models can only understand short pieces of text, which is a challenge when dealing with longer clinical notes. To fix this, they looked at three different ways to adapt the models so they could handle longer texts. They tested these new models on 16 tasks and found that one approach – using an English model trained on French medical texts – worked best. This shows that bigger language models can help improve results for most tasks, but BERT-based models are still really good at recognizing specific names in text. |
Keywords
» Artificial intelligence » Bert » Named entity recognition » Token
