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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
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Foundation Models for Natural Language Processing - Pre-trained Language Models Integrating Media (Hardcover, 1st ed. 2023)
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Foundation Models for Natural Language Processing - Pre-trained Language Models Integrating Media (Hardcover, 1st ed. 2023)
Series: Artificial Intelligence: Foundations, Theory, and Algorithms
Expected to ship within 12 - 17 working days
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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 can 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.
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