Fine-Tuning LLMs for Specialized Use Cases DOI Creative Commons
D. M. Anisuzzaman, Jeffrey G. Malins, Paul A. Friedman

и другие.

Mayo Clinic Proceedings Digital Health, Год журнала: 2024, Номер 3(1), С. 100184 - 100184

Опубликована: Ноя. 29, 2024

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

Язык: Английский

Fine-Tuning LLMs for Specialized Use Cases DOI Creative Commons
D. M. Anisuzzaman, Jeffrey G. Malins, Paul A. Friedman

и другие.

Mayo Clinic Proceedings Digital Health, Год журнала: 2024, Номер 3(1), С. 100184 - 100184

Опубликована: Ноя. 29, 2024

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

Язык: Английский

Процитировано

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