Search Results for author:
Found 4 papers, 3 papers with code
Exploration of Masked and Causal Language Modelling for Text Generation
In addition, we evaluate the usefulness of the generated texts by using them in three different downstream tasks: 1) Entity Recognition, 2) Text Classification, and 3) Authorship Verification.
Generating Medical Prescriptions with Conditional Transformer
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.
Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts
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}).
Exploring the Value of Pre-trained Language Models for Clinical Named Entity Recognition
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.
