1 code implementation • 30 Oct 2023 • Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Warren Del-Pinto, Goran Nenadic
LT3 is trained on a set of around 2K lines of medication prescriptions extracted from the MIMIC-III database, allowing the model to produce valuable synthetic medication prescriptions.
1 code implementation • 22 Sep 2023 • Zihao Li, Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Matthew Shardlow, Goran Nenadic
In this work, we investigate the ability of state-of-the-art large language models (LLMs) on the task of biomedical abstract simplification, using the publicly available dataset for plain language adaptation of biomedical abstracts (\textbf{PLABA}).
2 code implementations • 23 Oct 2022 • Samuel Belkadi, Lifeng Han, Yuping Wu, Goran Nenadic
The experimental outcomes show that 1) CRF layers improved all language models; 2) referring to BIO-strict span level evaluation using macro-average F1 score, although the fine-tuned LLMs achieved 0. 83+ scores, the TransformerCRF model trained from scratch achieved 0. 78+, demonstrating comparable performances with much lower cost - e. g. with 39. 80\% less training parameters; 3) referring to BIO-strict span-level evaluation using weighted-average F1 score, ClinicalBERT-CRF, BERT-CRF, and TransformerCRF exhibited lower score differences, with 97. 59\%/97. 44\%/96. 84\% respectively.