Best 5 Foundational Review Papers on Large Language Models

5 Review Papers on Large Language Models You Shouldn’t Miss

Large language models (LLMs) have emerged as a transformative force in natural language processing, with applications spanning text generation, question answering, summarization, and many other areas. 

This article provides a curated review of five seminal papers that lay the groundwork for understanding LLMs and their core concepts. The papers covered offer comprehensive surveys analyzing the architectures, training procedures, capabilities, and limitations of state-of-the-art LLMs. 

Key topics include the self-attention mechanism enabling long-range context modeling, pretraining objectives like masked language modeling, and retrieval-augmented approaches leveraging external knowledge sources. 

The review also examines emerging trends, open challenges, and future research directions in this rapidly evolving field. By synthesizing these foundational works, this article provides researchers and practitioners with a thorough grounding in the principles underpinning LLMs and their real-world applications.