Concept Drift in Large Language Models: Adapting the Conversation, 1st Edition eBook

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This book explores the application of the complex relationship between concept drift and cutting-edge large language models to address the problems and opportunities in navigating changing data landscapes. It discusses the theoretical basis of concept drift and its consequences for large language models, particularly the transformative power of cutting-edge models such as GPT-3.5 and GPT-4. It offers real-world case studies to observe firsthand how concept drift influences the performance of language models in a variety of circumstances, delivering valuable lessons learnt and actionable takeaways. The book is designed for professionals, AI practitioners, and scholars, focused on natural language processing, machine learning, and artificial intelligence. Examines concept drift in AI, particularly its impact on large language models Analyses how concept drift affects large language models and its theoretical and practical consequences Covers detection methods and practical implementation challenges in language models Showcases examples of concept drift in GPT models and lessons learnt from their performance Identifies future research avenues and recommendations for practitioners tackling concept drift in large language models

This is a digital product.

Concept Drift in Large Language Models: Adapting the Conversation 1st Edition is written by Ketan Sanjay Desale and published by Chapman & Hall. The Digital and eTextbook ISBNs for Concept Drift in Large Language Models are 9781040341438, 1040341438 and the print ISBNs are 9781032978079, 1032978074. Additional ISBNs for this eTextbook include 9781003595533, 9781040341407.