research-article

When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications

Authors:

Yefeng ZhengAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1104 - 1114

https://doi.org/10.1145/3626772.3657722

Published: 11 July 2024 Publication History

Abstract

The recent surge in Large Language Models (LLMs) has garnered significant attention across numerous fields. Fine-tuning is often required to fit general LLMs for a specific domain, like the web-based healthcare system. However, two problems arise during fine-tuning LLMs for medical applications. One is the task variety problem, which involves distinct tasks in real-world medical scenarios. The variety often leads to sub-optimal fine-tuning for data imbalance and seesaw problems. Besides, the large amount of parameters in LLMs leads to huge time and computation consumption by fine-tuning. To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA. The designed framework aims to absorb both the benefits of mixture-of-expert (MOE) for multi-task learning and low-rank adaptation (LoRA) for parameter efficient fine-tuning. For unifying MOE and LoRA, we devise multiple experts as the trainable parameters, where each expert consists of a pair of low-rank matrices to retain the small size of trainable parameters. Then, a task-motivated gate function for all MOELoRA layers is proposed, which can control the contributions of each expert and produce distinct parameters for various tasks. We conduct experiments on a multi-task medical dataset, indicating MOELoRA outperforms the existing parameter efficient fine-tuning methods. The code is available online.

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Almubark I(2025)Exploring the Impact of Large Language Models on Disease DiagnosisIEEE Access10.1109/ACCESS.2025.352702513(8225-8238)Online publication date: 2025
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Chen HXu YXu YZhao YZhang YCui L(2025)Effective test-time personalization for federated semantic segmentationExpert Systems with Applications10.1016/j.eswa.2025.126642272(126642)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126642
Mao YGe YFan YXu WMi YHu ZGao Y(2025)A survey on LoRA of large language modelsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40663-919:7Online publication date: 1-Jul-2025
https://dl.acm.org/doi/10.1007/s11704-024-40663-9
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Index Terms

When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

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Published: 11 July 2024

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SIRG - CityU Strategic Interdisciplinary Research Grant
CCF-Tencent Open Fund
CityU - HKIDS Early Career Research Grant
APRC - CityU New Research Initiatives
Hong Kong Environmental and Conservation Fund
Research Impact Fund
Hong Kong ITC Innovation and Technology Fund Midstream Research Programme for Universities Project

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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 14 - 18, 2024

Washington DC, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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https://doi.org/10.1109/ACCESS.2025.3527025
Chen HXu YXu YZhao YZhang YCui L(2025)Effective test-time personalization for federated semantic segmentationExpert Systems with Applications10.1016/j.eswa.2025.126642272(126642)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126642
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