DataHour: Large Language Models: Foundation, Evolution and Applications

This session will aim to simplify the core concepts behind large language models, which is the backbone of most of the advanced models in use today. We will start with the Transformer architecture (Google Brain, 2017), introduced in the seminal paper "Attention is All You Need". The Transformer model is a deep learning architecture that introduced a self-attention mechanism, enabling the model to weigh the importance of different parts of input data.

Building upon this knowledge, we will deep dive into a comprehensive exploration of the evolutionary journey of foundation models, starting with models like ELMo, GPT, and BERT in the late 2010s. These models, trained on vast, web-sourced datasets with self-supervised objectives, marked a shift from supervised learning that required annotated data. We will briefly discuss platforms like LangChain that enables developers to streamline the development of real-world applications by facilitating the integration of large language models with external data sources and components. We will conclude the session with the practical applications of these algorithms in finance, healthcare, legal services etc. and will also address the challenges and ethical considerations intrinsic to the development and deployment of these models, including issues concerning bias, privacy, and more.