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Cited by 186Topic ModelingDomain Adaptation and Few-Shot LearningNatural Language Processing Techniques

Parameter-efficient fine-tuning in large language models: a survey of methodologies

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Wang, Luping, Chen, Sheng, Jiang, Linnan, Pan, Shu, Cai, Runze, Yang, Sen, Yang, Fei·2025·Artificial Intelligence Review
DOI 10.1007/s10462-025-11236-4

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Abstract

Abstract The large models, as predicted by scaling law forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. These large models require substantial computational resources and GPU memory to operate. When adapting large models to specific downstream tasks, their massive parameter scale poses a significant challenge in fine-tuning on hardware platforms with limited computational power and GPU memory. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) offers a practical solution by efficiently adjusting the parameters of large pre-trained models to suit various downstream tasks. Specifically, PEFT adjusts the parameters of pre-trained large models to adapt to specific tasks or domains, minimizing the introduction of additional parameters and the computational resources required. This review mainly introduces the preliminary knowledge of PEFT, the core ideas and principles of various PEFT algorithms, the applications of PEFT, and potential future research directions. By reading this review, we believe that interested parties can quickly grasp the PEFT methodology, thereby accelerating its development and innovation.

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  • Parameter-efficient fine-tuning using limited labeled data can substantially enhance LLM performance under specific patient populations or device conditions.

    “Additionally, parameter-efficient fine-tuning (PEFT) using limited labeled data can substantially enhance LLM performance under specific patient populations or device conditions, facilitating diverse downstream applications [145].”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [145]

    The Introduction says PEFT adapts LLMs to new tasks and improves performance, and the Conclusions say it supports varied downstream tasks. The survey does not establish that limited labeled data substantially improves performance for specific patient populations or device conditions; its Future directions section instead warns that fine-tuning on modest datasets can produce overconfident predictions. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Wang, L., Chen, S., Jiang, L., Pan, S., Cai, R., Yang, S., & Yang, F.
Affiliations
Zhejiang Lab (CN)
Corresponding author
Yang, Fei
ORCID
Venue
Artificial Intelligence Review
Publisher
Springer Science and Business Media LLC
Published
2025-05-03
Volume
58
Issue
8
ISSN
  • 0269-2821 (ISSN-L)
  • 1573-7462
DOI
10.1007/s10462-025-11236-4
OpenAlex
W4410061981
Cited by
186
References
185
Citations by year
2026: 139 · 2025: 47
FWCI
75.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Artificial Intelligence Review (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
Funders
  • National Key Research and Development Program of China (2023YFE0108600)
  • National Natural Science Foundation of China (U22A6001)
  • Shanghai Artificial Intelligence Laboratory (P22KN00581)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
parameter-efficient fine-tuning, large language models, GPU memory constraints, computational efficiency, natural language generation, domain adaptation, scaling laws

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