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The review aims to survey current large language models and their architectures, including methods of fine-tuning and applications.

April 22, 2026Open Access

Large Language Models: Architectures, Fine-Tuning, and Retrieval-Augmented Generation — A Comprehensive Review

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This comprehensive review discusses language model architectures and fine-tuning strategies, highlighting advancements and challenges in the field.

The review aims to survey current large language models and their architectures, including methods of fine-tuning and applications.
Survey of transformer-based architectures including BERT, GPT, and T5.
Discussion of fine-tuning techniques and retrieval-augmented generation strategies.
Overview of challenges and future research directions.
Highlights advancements in model architectures and pre-training techniques.
Identifies key challenges like hallucination and computational cost.
Examines implications of alignment techniques and parameter-efficient fine-tuning.

Fatima et al. (2026) studied this question.

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This comprehensive review discusses language model architectures and fine-tuning strategies, highlighting advancements and challenges in the field.