Early Language Models - Historical Context
Part 1 of the Introduction to LLM Series, we start out by exploring the Historical Context and Evolution of early pretrained neural language models.
It presents a study on Large Language Models (LLMs), spotlighting their advancements post-ChatGPT's 2022 release. It emphasizes LLMs' proficiency in natural language tasks, attributed to extensive training on vast datasets, aligning with scaling laws. It aims to review notable LLMs, particularly focusing on the GPT, LLaMA, and PaLM families, analyzing their features, impacts, and limitations. Additionally, it explores the methodologies for constructing and enhancing LLMs, alongside evaluating popular datasets and metrics used in LLM training and assessment. A comparative analysis of LLM performance across various benchmarks is provided, aiming to highlight the strengths and weaknesses of each model. The conclusion addresses the challenges and future research directions in LLM development, aiming to outline the path forward in addressing computational efficiency, data bias, and ethical considerations in the continuous evolution of LLM technology. This summary encapsulates the paper's comprehensive examination of LLMs' current state, contributions to NLP, and prospective areas for innovation.
#largelanguagemodels #ai #gpt4 #palm2 #llama2 #artificialintelligence #llm #bert #deberta #xlm #xlnet #encoderdecoderplm #encoderplm #decoderplm
This video is mostly based on the paper "Large Language Models: A Survey" by Minaee, Mikolov, Nikzad, Changhlu, Socher, Amatriain, and Gao 2024 ( arXiv:2402.06196v1 [cs.CL] 9 Feb 2024 )