LLM Fine-Tuning: Concepts, Opportunities, and Challenges Article Swipe
Xiao-Kun Wu
,
Min Chen
,
Wanyi Li
,
Rui Wang
,
Lingbin Lu
,
Jia Liu
,
Kai Hwang
,
Yixue Hao
,
Yi Pan
,
Qingguo Meng
,
Kaibin Huang
,
Long Hu
,
Mohsen Guizani
,
Naipeng Chao
,
Giancarlo Fortino
,
Fei Lin
,
Yonglin Tian
,
Dusit Niyato
,
Fei-Yue Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc9040087
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc9040087
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bdcc9040087
- https://www.mdpi.com/2504-2289/9/4/87/pdf?version=1743564064
- OA Status
- gold
- Cited By
- 17
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409098775
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409098775Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bdcc9040087Digital Object Identifier
- Title
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LLM Fine-Tuning: Concepts, Opportunities, and ChallengesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-02Full publication date if available
- Authors
-
Xiao-Kun Wu, Min Chen, Wanyi Li, Rui Wang, Lingbin Lu, Jia Liu, Kai Hwang, Yixue Hao, Yi Pan, Qingguo Meng, Kaibin Huang, Long Hu, Mohsen Guizani, Naipeng Chao, Giancarlo Fortino, Fei Lin, Yonglin Tian, Dusit Niyato, Fei-Yue WangList of authors in order
- Landing page
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https://doi.org/10.3390/bdcc9040087Publisher landing page
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https://www.mdpi.com/2504-2289/9/4/87/pdf?version=1743564064Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2504-2289/9/4/87/pdf?version=1743564064Direct OA link when available
- Concepts
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Fine-tuning, Computer science, Data science, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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17Total citation count in OpenAlex
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2025: 17Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.value | 0.99927761 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |