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

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

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 Resid...

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Bibliographic Details
Main Authors: Thanh-Dung Le, Ti Ti Nguyen, Vu Nguyen Ha, Symeon Chatzinotas, Philippe Jouvet, Rita Noumeir
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Low-rank adaptation (LoRA)
adapters
LLM
clinical NLP
cardiac failure
text classification
Online Access:https://ieeexplore.ieee.org/document/11048527/
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