LLM Fine-Tuning: Concepts, Opportunities, and Challenges

As a foundation of large language models, fine-tuning drives rapid progress, broad applicability, and profound impacts on human–AI collaboration, surpassing earlier technological advancements. This paper provides a comprehensive overview of large language model (LLM) fine-tuning by integrating hermeneutic theories of human comprehension, with a focus on the essential cognitive conditions that underpin this process. Drawing on Gadamer’s concepts of Vorverständnis, Distanciation, and the Hermeneutic Circle, the paper explores how LLM fine-tuning evolves from initial learning to deeper comprehension, ultimately advancing toward self-awareness. It examines the core principles, development, and applications of fine-tuning techniques, emphasizing its growing significance across diverse field and industries. The paper introduces a new term, “Tutorial Fine-Tuning (TFT)”, which annotates a process of intensive tuition given by a “tutor” to a small number of “students”, to define the latest round of LLM fine-tuning advancements. By addressing key challenges associated with fine-tuning, including ensuring adaptability, precision, credibility and reliability, this paper explores potential future directions for the co-evolution of humans and AI. By bridging theoretical perspectives with practical implications, this work provides valuable insights into the ongoing development of LLMs, emphasizing their potential to achieve higher levels of cognitive and operational intelligence.

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Publication Details

Journal
Big Data and Cognitive Computing
Published
2025-04-02
DOI
https://doi.org/10.3390/bdcc9040087
Citations
41
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
76.68

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article

LLM Fine-Tuning: Concepts, Opportunities, and Challenges

Naipeng Chao, Wanyi Li, Giancarlo Fortino, Fei-Yue Wang et al.
41 citations
Big Data and Cognitive Computing
Magnetic confinement fusion research
76.68
article

LLM Fine-Tuning: Concepts, Opportunities, and Challenges

Naipeng Chao, Wanyi Li, Giancarlo Fortino, Fei-Yue Wang, Xiao-Kun Wu, Yixue Hao, Yonglin Tian, Kai Hwang, Lingbin Lu, Mohsen Guizani, Long Hu, Fei Lin, Dusit Niyato, Jia Liu, Kaibin Huang, Min Chen, Yi Pan, Qingguo Meng, Rui Wang
article en
41 citations

Abstract

As a foundation of large language models, fine-tuning drives rapid progress, broad applicability, and profound impacts on human–AI collaboration, surpassing earlier technological advancements. This paper provides a comprehensive overview of large language model (LLM) fine-tuning by integrating hermeneutic theories of human comprehension, with a focus on the essential cognitive conditions that underpin this process. Drawing on Gadamer’s concepts of Vorverständnis, Distanciation, and the Hermeneutic Circle, the paper explores how LLM fine-tuning evolves from initial learning to deeper comprehension, ultimately advancing toward self-awareness. It examines the core principles, development, and applications of fine-tuning techniques, emphasizing its growing significance across diverse field and industries. The paper introduces a new term, “Tutorial Fine-Tuning (TFT)”, which annotates a process of intensive tuition given by a “tutor” to a small number of “students”, to define the latest round of LLM fine-tuning advancements. By addressing key challenges associated with fine-tuning, including ensuring adaptability, precision, credibility and reliability, this paper explores potential future directions for the co-evolution of humans and AI. By bridging theoretical perspectives with practical implications, this work provides valuable insights into the ongoing development of LLMs, emphasizing their potential to achieve higher levels of cognitive and operational intelligence.

Big Data and Cognitive ComputingVol. 9(4)
Macau University of Science and Technology (MO), Nanyang Technological University (SG), Shenzhen University (CN), Chinese Academy of Sciences (CN), Institute of Automation (CN), Mohamed bin Zayed University of Artificial Intelligence (AE), Chinese University of Hong Kong, Shenzhen (CN), University of Calabria (IT), Huazhong University of Science and Technology (CN), Renmin University of China (CN), University of Hong Kong (HK), South China University of Technology (CN), Tsinghua University (CN)
National Social Science Fund of China
Openalex Percentile: Top 0%
Magnetic confinement fusion research
76.68
DOIOpen Access PDFOpenAlex
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