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Total Size:
8.7 MB
Info Hash:
BC77C11AC7638ECC3C570E948EFE063F7DE4FEF9
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Added:
April 21, 2026, 1:28 p.m.
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(Last updated: April 21, 2026, 1:33 p.m.)
| File | Size |
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| Desale K. Concept Drift in Large Language Models. Adapting the Conversation 2025.pdf | 8.7 MB |
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NOTE
SOURCE: Desale K. Concept Drift in Large Language Models. Adapting the Conversation 2025
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COVER
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MEDIAINFO
Textbook in PDF format This book explores the application of the complex relationship between concept drift and cutting-edge large language models (LLMs) 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 (NLP), Machine Learning, and Artificial Intelligence (AI). Large language models are ubiquitously relevant in a broad range of applications, and their diverse capabilities have the potential to drastically alter the Artificial Intelligence landscape. These models represent the cutting edge of natural language processing (NLP), exhibiting unmatched expertise in a range of applications, including sentiment analysis, machine translation, question answering and text summarization. Their ability to produce human-like text goes beyond simple language jobs; they can also be used to generate creative prose, poetry and even computer code, demonstrating their diversity in content production. Additionally, these models form the basis for conversational AI development, which makes it easier to construct sophisticated chatbots and virtual assistants that can engage with users in a more natural and context-aware manner, transforming human-computer interfaces. Large language models are excellent at parsing and extracting structured information from unstructured text by utilizing their knowledge extraction capabilities. Large language models such as GPT-3.5 and GPT-4 have established their significance in the world of AI. Their capabilities, applications and relevance in addressing complex language tasks are undeniable, making them essential tools for modern AI research and development. 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
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