Exploring The Landscape Of Large Language Models: Foundations, Techniques, And Challenges

Moradi Milad, Yan Ke, Colwell David, Samwald Matthias, Asgari Rhona. Arxiv 2024

[Paper]    
Agentic Applications Efficiency And Optimization Fine Tuning In Context Learning Merging Pretraining Methods Prompting Reinforcement Learning Survey Paper Tools Training Techniques

In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes. The article sheds light on the mechanics of in-context learning and a spectrum of fine-tuning approaches, with a special focus on methods that optimize efficiency in parameter usage. Additionally, it explores how LLMs can be more closely aligned with human preferences through innovative reinforcement learning frameworks and other novel methods that incorporate human feedback. The article also examines the emerging technique of retrieval augmented generation, integrating external knowledge into LLMs. The ethical dimensions of LLM deployment are discussed, underscoring the need for mindful and responsible application. Concluding with a perspective on future research trajectories, this review offers a succinct yet comprehensive overview of the current state and emerging trends in the evolving landscape of LLMs, serving as an insightful guide for both researchers and practitioners in artificial intelligence.

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