Article

For clinical data extraction, QLoRA attains accuracy close to LoRA while requiring lower compute resources

2025-10-23

Abstract excerpt

<h4>Background</h4> Large language models (LLMs) can accurately extract structured data from free text, yet fine-tuning for specific clinical tasks is often compute- and memory-intensive. We examine whether Parameter-Efficient Fine-Tuning (PEFT)—updating only a small subset of weights in the LLM— preserves accuracy on quantized models while further reducing memory and graphical processing unit (GPU) requirements...

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Literature Corpus work: 5d5e2060-2a22-5c6f-a7ba-47db8dae7566
DOI: 10.1101/2025.10.21.25338506

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