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

• URL: http://arxiv.org/abs/2407.19299v3
• Date: Mon, 28 Jul 2025 12:20:24 GMT
• Title: The Impact of LoRA Adapters on LLMs for Clinical Text Classification Under Computational and Data Constraints
• Authors: Thanh-Dung Le, Ti Ti Nguyen, Vu Nguyen Ha, Symeon Chatzinotas, Philippe Jouvet, Rita Noumeir,
• Abstract summary: Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to domain gap, limited data, and stringent hardware constraints.<n>In this study, we evaluate four adapter techniques-Adapter, Lightweight, TinyAttention, and Gated Residual Network (GRN) - equivalent to Low-Rank Adaptation (LoRA)<n>All experiments were conducted on a single NVIDIA P620 GPU (2 GB VRAM, 512 cores, 1.386 TFLOPS FP32), limiting batch sizes to 2568 and sequence length to 2568 tokens.
• Score: 32.23468444597675
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to domain gap, limited data, and stringent hardware constraints. In this study, we evaluate four adapter techniques-Adapter, Lightweight, TinyAttention, and Gated Residual Network (GRN) - equivalent to Low-Rank Adaptation (LoRA), for clinical note classification under real-world, resource-constrained conditions. All experiments were conducted on a single NVIDIA Quadro P620 GPU (2 GB VRAM, 512 CUDA cores, 1.386 TFLOPS FP32), limiting batch sizes to <8 sequences and maximum sequence length to 256 tokens. Our clinical corpus comprises only 580 000 tokens, several orders of magnitude smaller than standard LLM pre-training datasets. We fine-tuned three biomedical pre-trained LLMs (CamemBERT-bio, AliBERT, DrBERT) and two lightweight Transformer models trained from scratch. Results show that 1) adapter structures provide no consistent gains when fine-tuning biomedical LLMs under these constraints, and 2) simpler Transformers, with minimal parameter counts and training times under six hours, outperform adapter-augmented LLMs, which required over 1000 GPU-hours. Among adapters, GRN achieved the best metrics (accuracy, precision, recall, F1 = 0.88). These findings demonstrate that, in low-resource clinical settings with limited data and compute, lightweight Transformers trained from scratch offer a more practical and efficient solution than large LLMs, while GRN remains a viable adapter choice when minimal adaptation is needed.

Related papers

• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• Structuring Radiology Reports: Challenging LLMs with Lightweight Models [5.01440254761063]
  Large language models (LLMs) have shown strong capabilities in reformatting clinical text, their high computational requirements, lack of transparency, and data privacy concerns hinder practical deployment.<n>We explore lightweight encoder-decoder models (300M parameters)-specifically T5 and BERT2BERT-for structuring radiology reports from the MIMIC-CXR and CheXpert Plus datasets.<n>Our best-performing lightweight model outperforms all LLMs adapted using prompt-based techniques on a human-annotated test set.
  arXiv  Detail & Related papers  (2025-05-30T20:12:51Z)
• Quantizing Large Language Models for Code Generation: A Differentiated Replication [51.85505914274633]
  Large Language Models (LLMs) have shown an impressive capability in code generation and, specifically, to automatically implement requirements described in natural language.<n>LLMs pose significant challenges related to their memory (and, consequently, carbon) footprint.<n>New frontier for LLM quantization is 4-bit precision, resulting in an average memory footprint reduction of 70%.
  arXiv  Detail & Related papers  (2025-03-10T09:26:08Z)
• Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages [10.418542753869433]
  Low-resource languages (LRLs) face significant challenges in natural language processing (NLP) due to limited data. Current state-of-the-art large language models (LLMs) still struggle with LRLs. Small multilingual models (mLMs) such as mBERT and XLM-R offer greater promise due to a better fit of their capacity to low training data sizes.
  arXiv  Detail & Related papers  (2025-02-14T13:10:39Z)
• Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
  Large language models (LLMs) have significantly improved language understanding and generation capabilities. LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands. We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
  arXiv  Detail & Related papers  (2024-12-19T18:08:04Z)
• Using Large Language Models for Expert Prior Elicitation in Predictive Modelling [53.54623137152208]
  This study proposes using large language models (LLMs) to elicit expert prior distributions for predictive models. We compare LLM-elicited and uninformative priors, evaluate whether LLMs truthfully generate parameter distributions, and propose a model selection strategy for in-context learning and prior elicitation. Our findings show that LLM-elicited prior parameter distributions significantly reduce predictive error compared to uninformative priors in low-data settings.
  arXiv  Detail & Related papers  (2024-11-26T10:13:39Z)
• Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs [75.11449420928139]
  Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a promising solution, but there exists a gap between the practical performance of low-rank adaptations and its theoretical optimum. We propose eXtreme Gradient Boosting LoRA, a novel framework that bridges this gap by leveraging the power of ensemble learning.
  arXiv  Detail & Related papers  (2024-10-25T17:07:13Z)
• STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data [2.0738462952016232]
  This paper presents the first application of spiking neural networks (SNNs) for the classification of chronic lower back pain (CLBP) using the EmoPain dataset. We introduce Spike Threshold Adaptive Learning (STAL), a trainable encoder that effectively converts continuous biosignals into spike trains. We also propose an ensemble of Spiking Recurrent Neural Network (SRNN) classifiers for the multi-stream processing of sEMG and IMU data.
  arXiv  Detail & Related papers  (2024-07-11T10:15:52Z)
• Geneverse: A collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research [20.285114234576298]
  Large language models (LLMs) are promising for biomedical and healthcare research. We propose a collection of finetuned LLMs and multimodal LLMs (MLLMs) for three novel tasks in genomics and proteomic research. The models in Geneverse are trained and evaluated based on domain-specific datasets. We demonstrate that adapted LLMs and MLLMs perform well for these tasks and may outperform closed-source large-scale models.
  arXiv  Detail & Related papers  (2024-06-21T14:19:10Z)
• ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization [13.622268474310918]
  ShiftAddLLM is an efficient multiplication-free model for large language models. It achieves perplexity improvements of 5.6 and 22.7 points at comparable or lower latency. Experiments on five LLM families and eight tasks consistently validate the effectiveness of ShiftAddLLM.
  arXiv  Detail & Related papers  (2024-06-10T02:47:55Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits [129.6765656933016]
  We introduce a 1-bit Large Language Models (LLMs) variant, namely BitNet b1.58. The 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs. It enables a new paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.
  arXiv  Detail & Related papers  (2024-02-27T18:56:19Z)
• FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing [10.47214968497857]
  We present high-performance methods that exploit low-rank structures to pretrain and finetune large language models. Our methods achieve a speedup of 1.3X and a model compression ratio of 2.64X for pretaining without accuracy drop. For finetuning, our methods achieve an average accuracy increase of 6.3% and 24.0% in general tasks and financial tasks.
  arXiv  Detail & Related papers  (2024-02-21T05:03:17Z)
• BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [53.31402059062365]
  BiLLM is a groundbreaking 1-bit post-training quantization scheme tailored for pretrained large language models. It achieves for the first time high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLMs families.
  arXiv  Detail & Related papers  (2024-02-06T09:26:34Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification [1.4715634464004446]
  Large language models (LLMs) have demonstrated promising transfer learning capability. LLMs demonstrated the potential to speed up the execution of clinical NLP studies by reducing the need for curating large annotated datasets. This may result in an increase in the utilization of NLP-based variables and outcomes in observational clinical studies.
  arXiv  Detail & Related papers  (2024-01-25T02:05:31Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning [54.682106515794864]
  offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. This paper introduces $textbfLanguage Models for $textbfMo$tion Control ($textbfLaMo$), a general framework based on Decision Transformers to use pre-trained Language Models (LMs) for offline RL. Empirical results indicate $textbfLaMo$ achieves state-of-the-art performance in sparse-reward tasks.
  arXiv  Detail & Related papers  (2023-10-31T16:24:17Z)
• LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models [75.25782573728677]
  This paper presents a framework for adapter-based parameter-efficient fine-tuning (PEFT) of language models (LLMs) The framework includes state-of-the-art open-access LLMs such as LLaMA, BLOOM, and GPT-J, as well as widely used adapters such as Series adapters, Parallel adapter, Prompt-based learning and Reparametrization-based methods. We evaluate the effectiveness of the adapters on fourteen datasets from two different reasoning tasks, Arithmetic Reasoning and Commonsense Reasoning.
  arXiv  Detail & Related papers  (2023-04-04T16:31:37Z)
• Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines [4.876281217951695]
  NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. We developed digital pathology pipelines to benchmark the five most recently proposed NLP models and four popular CNN models. Our NLP models achieved state-of-the-art predictions for all three biomarkers using a relatively small training dataset.
  arXiv  Detail & Related papers  (2023-02-21T02:42:03Z)
• Exploring the Value of Pre-trained Language Models for Clinical Named Entity Recognition [6.917786124918387]
  We compare Transformer models that are trained from scratch to fine-tuned BERT-based LLMs. We examine the impact of an additional CRF layer on such models to encourage contextual learning.
  arXiv  Detail & Related papers  (2022-10-23T16:27:31Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.