LLM Fine-Tuning: Concepts, Opportunities, and Challenges

Checked 1 Oct 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

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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