Fine-Tuning LLMs for Specialized Use Cases

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Large language models (LLMs) are a type of artificial intelligence, and 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 pre-trained model, such as an LLM, is further trained on a custom dataset to adapt it for specialized tasks or domains. In this review, we outline some of the major methodological 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 methodological 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.