MARC details
| 000 -LEADER |
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| fixed length control field |
02840nam a2200217 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
53331 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
0000000000 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240411195500.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230612n s 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
978-3-031-23190-2 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Paaß, Gerhard. |
| 245 10 - TITLE STATEMENT |
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| Title |
Foundation models for natural language processing |
| Medium |
[electronic resource] : |
| Remainder of title |
pre-trained language models integrating media / |
| Statement of responsibility, etc. |
Gerhard Paaß, Sven Giesselbach. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cham, Switzerland : |
| Name of publisher, distributor, etc. |
Springer, |
| Date of publication, distribution, etc. |
2023. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource. |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Artifcial Intelligence: Foundations, Theory, and Algorithms |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Natural language processing (Computer science) |
| Source of heading or term |
sears |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Giesselbach, Sven, |
| Relator term |
Author. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1ULV8XcaLXRGDDbSE_uTcM5TzKiGDDTjI/view?usp=sharing">https://drive.google.com/file/d/1ULV8XcaLXRGDDbSE_uTcM5TzKiGDDTjI/view?usp=sharing</a> |
