How LLMs understand & generate human language

• Titel: How LLMs understand & generate human language
• Mitwirkende: Harwood, Kate [MitwirkendeR]
• Institutionen: Pearson (Firm), [Verlag]
• Ausgabe: [First edition].
• Verlagsort: [Place of publication not identified]
• Verlag: Pearson
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (1 video file (1 hr., 55 min.))
• Illustrationen: sound, color.
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed October 10, 2024)
• ISBN: 978-0-13-541430-9
• ISBN: 0-13-541430-X
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Traitement automatique des langues naturelles
• Sach-SW: Linguistique informatique
• Sach-SW: Intelligence artificielle
• Sach-SW: Apprentissage automatique
• Sach-SW: computational linguistics
• Sach-SW: artificial intelligence
• Sach-SW: Instructional films
• Sach-SW: Nonfiction films
• Sach-SW: Internet videos
• Sach-SW: Films de formation
• Sach-SW: Films autres que de fiction
• Sach-SW: Vidéos sur Internet
• K10plus-PPN: 1907896759
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4611075133
• Lokale URL Inst.: Zum Volltext

Abstract

Generative language models, such as ChatGPT and Microsoft Bing, are becoming a daily tool for a lot of us, but these models remain black boxes to many. How does ChatGPT know which word to output next? How does it understand the meaning of the text you prompt it with? Everyone, from those who have never once interacted with a chatbot, to those who do so regularly, can benefit from a basic understanding of how these language models function. This course answers some of your fundamental questions about how generative AI works. In this course, you learn about word embeddings: not only how they are used in these models, but also how they can be leveraged to parse large amounts of textual information utilizing concepts such as vector storage and retrieval augmented generation. It is important to understand how these models work, so you know both what they are capable of and where their limitations lie.