The Science Behind LLMs: Training, Tuning, and Beyond

Welcome to SHIFTERLABS’ cutting-edge podcast series, an experiment powered by Notebook LM. In this episode, we delve into “Understanding LLMs: A Comprehensive Overview from Training to Inference,” an insightful review by researchers from Shaanxi Normal University and Northwestern Polytechnical University. This paper outlines the critical advancements in Large Language Models (LLMs), from foundational training techniques to efficient inference strategies.

 

Join us as we explore the paper’s analysis of pivotal elements, including the evolution from early neural language models to today’s transformer-based giants like GPT. We unpack detailed sections on data preparation, preprocessing methods, and architectures (from encoder-decoder models to decoder-only architectures). The discussion highlights parallel training, fine-tuning techniques such as Supervised Fine-Tuning (SFT) and parameter-efficient tuning, and groundbreaking approaches like Reinforcement Learning with Human Feedback (RLHF). We also examine future trends, safety protocols, and evaluation methods essential for LLM development and deployment.

 

This episode is part of SHIFTERLABS’ mission to inform and inspire through the fusion of research, technology, and education. Dive in to understand what makes LLMs the cornerstone of modern AI and how this knowledge shapes their application in real-world scenarios.