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Large Language Models

Fine-Tuning Large Language Models for Specialized Use Cases

DM Anisuzzaman(Mayo Clinic in Arizona), Jeffrey G. Malins(Mayo Clinic in Arizona), Paul A. Friedman(Mayo Clinic in Arizona), Zachi I. Attia(Mayo Clinic in Arizona)
November 29, 2024Mayo Clinic Proceedings Digital Health158 citations
PDFDOI

158

Citations

2

Influential Citations

Mayo Clinic Proceedings Digital Health

Venue

2024

Year

Abstract

Large language models (LLMs) are a type of artificial intelligence, which operate by predicting and assembling sequences of words that are statistically likely to follow from a given text input. With this basic ability, LLMs are able to answer complex questions and follow extremely complex instructions. Products created using LLMs such as ChatGPT by OpenAI and Claude by Anthropic have created a huge amount of traction and user engagements and revolutionized the way we interact with technology, bringing a new dimension to human-computer interaction. Fine-tuning is a process in which a pretrained model, such as an LLM, is further trained on a custom data set to adapt it for specialized tasks or domains. In this review, we outline some of the major methodologic approaches and techniques that can be used to fine-tune LLMs for specialized use cases and enumerate the general steps required for carrying out LLM fine-tuning. We then illustrate a few of these methodologic approaches by describing several specific use cases of fine-tuning LLMs across medical subspecialties. Finally, we close with a consideration of some of the benefits and limitations associated with fine-tuning LLMs for specialized use cases, with an emphasis on specific concerns in the field of medicine.

Analysis

Why This Paper Matters

Large language models have demonstrated remarkable general-purpose capabilities, but their utility in specialized domains—particularly medicine—depends on effective fine-tuning. This review, published in Mayo Clinic Proceedings Digital Health, addresses a critical gap by consolidating fragmented knowledge about fine-tuning methodologies into a coherent framework. As healthcare increasingly explores AI integration, practitioners need clear guidance on adapting LLMs to clinical workflows, data formats, and regulatory requirements. The paper's emphasis on medical subspecialties makes it directly relevant to translational AI research.

Technical Contributions

The paper systematically categorizes fine-tuning approaches:

  • Full fine-tuning: Updating all model parameters on domain-specific data.
  • Parameter-efficient fine-tuning (PEFT): Methods like LoRA and adapter layers that modify only a small fraction of parameters, reducing computational cost.
  • Instruction tuning: Training the model to follow task-specific prompts.
  • Reinforcement learning from human feedback (RLHF): Aligning model outputs with human preferences.

The authors also outline a practical pipeline: data collection and preprocessing, base model selection, hyperparameter tuning, training, evaluation, and deployment. They illustrate these steps with concrete examples from radiology, pathology, and clinical decision support.

Results

As a review paper, no novel experimental results are reported. However, the paper synthesizes findings from 158 cited works, providing a meta-perspective on the effectiveness of different fine-tuning strategies. Key takeaways include the trade-off between full fine-tuning (higher accuracy but expensive) and PEFT (lower cost but potential performance loss), and the importance of high-quality, domain-specific datasets.

Significance

This paper fills an important educational role for the AI community, particularly for researchers and clinicians entering the LLM fine-tuning space. By demystifying the process and highlighting medical-specific concerns (e.g., patient privacy, data scarcity, regulatory compliance), it lowers the barrier to entry for specialized LLM applications. The review also underscores the need for rigorous evaluation benchmarks in medical NLP, a direction likely to influence future research.

large-language-modelsfine-tuningtransfer-learningmedical-ainlpdomain-adaptation
Large Language ModelsFine TuningInstruction Tuning

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