What question did this study set out to answer?

This research aims to simplify medical language for better patient understanding using efficient fine-tuning of large language models (LLMs).

June 12, 2026Open Access

Quantized Low-Rank Adaptation in Large Language Models for Clinical Text Simplification

Key Points

This research aims to simplify medical language for better patient understanding using efficient fine-tuning of large language models (LLMs).
Fine-tuning the Llama-3.1-8B-Instruct model using Quantized Low-Rank Adaptation (QLoRA) on the Med-EASi and Cochrane datasets.
Evaluation of multiple adapter designs including Lightweight LoRA and TinyAttention.
4-bit quantization to reduce the model's memory footprint for efficient fine-tuning on consumer-grade GPUs.
Achieved a Flesch-Kincaid grade level of 7.6 for MedEASI and 11.06 for Cochrane, indicating better readability.
Improved BERTScore values of 0.845 for MedEASI and 0.864 for Cochrane, showcasing semantic preservation.
Enhanced reading-ease by +76.2 and +57.3 points over the source texts, indicating substantial gains in simplification performance.

Abstract

Simplifying medical language is vital for effective healthcare communication, as patients often find it hard to understand complex clinical terms and lengthy, information-rich sentences. Recent advances in natural language processing (NLP) have led to powerful large language models(LLMs). However, their application in the medical domain remains constrained by substantial computational and hardware requirements. In this work, we investigate parameter-efficient fine-tuning strategies to adapt LLMs for simplifying clinical text while maintaining semantic fidelity and factual accuracy. Using Quantized Low-Rank Adaptation (QLoRA), we fine-tuned the Llama-3.1-8B-Instruct model on the Med-EASi and Cochrane datasets, achieving substantial gains in readability while preserving medical meaning. We evaluate multiple adapter designs, including Lightweight LoRA, TinyAttention, and standard LoRA with rank-4 decomposition, to analyze their effectiveness for domain-specific simplification tasks. Through 4-bit quantization of the base model combined with trainable low-rank adapters, the overall memory footprint is reduced by nearly an order of magnitude, enabling efficient fine-tuning on a single consumer-grade GPU. Our fine-tuned model achieves a Flesch-Kincaid grade level of 7.6 for MedEASI and 11.06 for the Cochrane dataset, along with an Automated Readability Index of 8.7 for MedEASI and 9.63 for the Cochrane dataset. Additionally, the model achieves BERTScore values of 0.845 and 0.864 on the MedEASI and Cochrane datasets, respectively, while improving reading-ease by +76.2 and +57.3 points over the source texts. Experimental results demonstrate improved simplification performance relative to selected baseline systems while preserving semantic similarity. These findings show that parameter-efficient fine-tuning methods can achieve performance comparable to full fine-tuning while significantly reducing computational cost. This efficiency supports deployment in resource-constrained healthcare settings and may improve patient access to complex clinical information.

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