  Fine-tuning Large Language Models (LLMs) for clinical Natural Language
Processing (NLP) poses significant challenges due to the domain gap and limited
data availability. This study investigates the effectiveness of various adapter
techniques, equivalent to Low-Rank Adaptation (LoRA), for fine-tuning LLMs in a
resource-constrained hospital environment. We experimented with four
structures-Adapter, Lightweight, TinyAttention, and Gated Residual Network
(GRN)-as final layers for clinical notes classification. We fine-tuned
biomedical pre-trained models, including CamemBERT-bio, AliBERT, and DrBERT,
alongside two Transformer-based models. Our extensive experimental results
indicate that i) employing adapter structures does not yield significant
improvements in fine-tuning biomedical pre-trained LLMs, and ii) simpler
Transformer-based models, trained from scratch, perform better under resource
constraints. Among the adapter structures, GRN demonstrated superior
performance with accuracy, precision, recall, and an F1 score of 0.88.
Moreover, the total training time for LLMs exceeded 1000 hours, compared to
under 6 hours for simpler transformer-based models, highlighting that LLMs are
more suitable for environments with extensive computational resources and
larger datasets. Consequently, this study demonstrates that simpler
Transformer-based models can be effectively trained from scratch, providing a
viable solution for clinical NLP tasks in low-resource environments with
limited data availability. By identifying the GRN as the most effective adapter
structure, we offer a practical approach to enhance clinical note
classification without requiring extensive computational resources.
