01181nam a2200265 i 4500991001353069707536110805s2010 fr a b 001 0 fre 9782914777704b14001032-39ule_instDip.to Filologia Class. e Scienze FilosoficheitafregrcTheon,Smyrnaeus389037Expositio rerum mathematicarum ad legendum Patonem utilium43858Lire Platon :le recours au savoir scientifique, arithmétique, musique, astronomie /Théon de Smyrne ; présentation, traduction et annotations par Joelle Delattre BiencourtToulouse :Anacharsis,c2010490 p. :ill. ;20 cmEssais. PhilologieBibliografia: p. [483]-490. IndiceDelattre, Joelle.b1400103202-04-1405-08-11991001353069707536LE007 880.1 Theon Smyrnaeus DEL 01.0112007000213056le007pE37.95-l- 00000.i1530982405-08-11Expositio rerum mathematicarum ad legendum Patonem utilium43858UNISALENTOle00705-08-11ma -frefr 0004499nam 22006615 450 991084715470332120251008153435.09783031231902303123190210.1007/978-3-031-23190-2(CKB)31377991100041(ODN)ODN0010068911(DE-He213)978-3-031-23190-2(EXLCZ)993137799110004120230523d2023 u| 0engurcn|---|||||txtrdacontentcrdamediacrrdacarrierFoundation Models for Natural Language Processing Pre-trained Language Models Integrating Media /by Gerhard Paaß, Sven Giesselbach1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resourceArtificial Intelligence: Foundations, Theory, and Algorithms,2365-306X9783031231896 3031231899 1. Introduction -- 2. Pre-trained Language Models -- 3. Improving Pre-trained Language Models -- 4. Knowledge Acquired by Foundation Models -- 5. Foundation Models for Information Extraction -- 6. Foundation Models for Text Generation -- 7. Foundation Models for Speech, Images, Videos, and Control -- 8. Summary and Outlook.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 tobasic 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.Artificial Intelligence: Foundations, Theory, and Algorithms,2365-306XNatural language processing (Computer science)Computational linguisticsArtificial intelligenceExpert systems (Computer science)Machine learningNatural Language Processing (NLP)Computational LinguisticsArtificial IntelligenceKnowledge Based SystemsMachine LearningNatural language processing (Computer science)Computational linguistics.Artificial intelligence.Expert systems (Computer science)Machine learning.Natural Language Processing (NLP).Computational Linguistics.Artificial Intelligence.Knowledge Based Systems.Machine Learning.006.35COM004000COM025000COM073000LAN009000bisacshPaaß Gerhard1830675Giesselbach Sven1736135BOOK9910847154703321Foundation models for natural language processing4401165UNINA