Artificial Intelligence Foundations Theory and Algorithms Foundation Models for Natural Language Processing - Gerhard Paaß Sven Giesselbach Kartoniert (TB)

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then they are fine-tuned for specific tasks which they can often solve with superhuman accuracy. When the models are large enough they c an be instructed by prompts to solve new tasks without any fine-tuning. Moreover they can be applied to a wide range of different media and problem domains ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT GPT and sequence-to-sequence transformer are described as well as the concepts of self-attention and context-sensitive embedding. Then different approaches to improving these models are discussed such as expanding the pre-training criteria increasing the length of input texts or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented e.g. question answering translation story generation dialog systems generating images from text etc. For each application area the strengths and weaknesses of current models are discussed and an outlook on further developments is given. In addition links are provided to freely available program code. A concluding chapter summarizes the economic opportunities mitigation of risks and potential developments of AI.