The Impact of LoRA Adapters on LLMs for Clinical Text Classification Under Computational and Data Constraints

Keyphrases

• Clinical Text Classification 100%
• Adapter 100%
• LoRa 100%
• Data Constraints 100%
• Large Language Models 100%
• Computational Constraints 100%
• Gated Residual Network 50%
• Token 33%
• Lightweight Transformer 33%
• GPU 33%
• Adaptation 16%
• Order of Magnitude 16%
• Clinical Setting 16%
• Training Time 16%
• Precision-recall 16%
• Hardware Constraints 16%
• NVIDIA 16%
• Resource-constrained 16%
• Constraint Condition 16%
• Low-resource 16%
• Batch Size 16%
• Pre-trained Language Model 16%
• Domain Gap 16%
• Precision Accuracy 16%
• Clinical Notes Classification 16%
• Consistent Gain 16%
• Metric Accuracy 16%
• Clinical Natural Language Processing 16%
• Maximum Length Sequence 16%
• Simple Transformer 16%
• FP32 16%
• Major Challenges 16%
• Fine-tuning Language Models 16%
• CamemBERT 16%
• CUDA Core 16%
• Augmented Large Language Models 16%
• Network Equivalent 16%
• Pre-trained Large Language Models 16%
• Pre-training Datasets 16%

Computer Science

• Text Classification 100%
• Large Language Model 100%
• Data Constraint 100%
• Residual Neural Network 42%
• Graphics Processing Unit 28%
• Natural Language Processing 14%
• Training Dataset 14%
• Clinical Setting 14%
• Maximum Length Sequence 14%
• Transformer Model 14%