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Found 7 papers, 5 papers with code
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression
This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subtasks A and B.
On the Effectiveness of Compact Biomedical Transformers
Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks.
Ranked #2 on
Named Entity Recognition (NER)
on BC2GM
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers
Different strategies have been proposed in the literature to alleviate these problems, with the aim to create effective compact models that nearly match the performance of their bloated counterparts with negligible performance losses.
Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints
Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP.
Lightweight Transformers for Clinical Natural Language Processing
To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks.
Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing
In this context, our study investigates the potential of instruction tuning for biomedical language processing, applying this technique to two general LLMs of substantial scale.
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks
The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods.
