Advances in Fine-tuning Large Language Models
Year: 2024
Type: article
Abstract: This article explores the cutting-edge techniques for fine-tuning Large Language Models (LLMs) to enhance their performance in specialized domains and tasks. It delves into three primary approaches: few-shot learning, prompt engineering, and domain-specific adaptation. The discusses the principles, implementation strategies, and applications of each technique, highlighting their potential to significantly improve LLM performance across various industries. By examining these advanced fine-tuning methods, the article aims to provide practitioners with a comprehensive understanding of the current state-of-the-art LLM adaptation, enabling them to make informed decisions when tailoring these powerful models to their unique requirements.
Author: Researcher
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6892864976
FWCI:
Cites:
Cited by:
Related to: