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Submit a ProposalRetrieval Augmented Generation for Natural Language Processing
One Line Description
Master the cutting-edge technology bridging the gap between massive AI capabilities and precise corporate reality with this essential guide, to overcoming LLM limitations and deploying secure, domain-specific Retrieval-Augmented Generation solutions across real-world industries.
Description
The natural language processing domain has witnessed remarkable growth due to the availability of diverse, high-volume data and advanced machine-learning techniques, particularly large language models. Large language models trained on massive datasets can perform diverse tasks ranging from machine translation to text generation. However, these models face challenges, such as factual inaccuracy, biases in data, and a lack of domain-specific knowledge. This book explores the Retrieval-Augmented Generation spectrum, focusing on current trends, challenges, and applications. It introduces large language models and their capabilities, followed by the issues faced, particularly the lack of domain-specific knowledge. It also covers the fundamentals of retrieval-augmented generation and the process of integrating information retrieval with text generation, explaining how retrieval-augmented generation bridges the gap between statistical learning and real-world information repositories. Different information retrieval techniques, generation models, and evaluation metrics like BLUE score, ROUGE score, and task-specific metrics to assess the effectiveness of the model are discussed. The book will cover critical security and privacy concerns, as well as ethical considerations and policies regarding retrieval-augmented generation. Different case studies on knowledge management using summarization techniques, personalized learning in the education sector, and customized chatbots for customer service show the vast potential of retrieval-augmented generation models. This essential guide gives a deep understanding of this transformative technology and how it is revolutionizing how humans interact with machines.
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The natural language processing domain has witnessed remarkable growth due to the availability of diverse, high-volume data and advanced machine-learning techniques, particularly large language models. Large language models trained on massive datasets can perform diverse tasks ranging from machine translation to text generation. However, these models face challenges, such as factual inaccuracy, biases in data, and a lack of domain-specific knowledge. This book explores the Retrieval-Augmented Generation spectrum, focusing on current trends, challenges, and applications. It introduces large language models and their capabilities, followed by the issues faced, particularly the lack of domain-specific knowledge. It also covers the fundamentals of retrieval-augmented generation and the process of integrating information retrieval with text generation, explaining how retrieval-augmented generation bridges the gap between statistical learning and real-world information repositories. Different information retrieval techniques, generation models, and evaluation metrics like BLUE score, ROUGE score, and task-specific metrics to assess the effectiveness of the model are discussed. The book will cover critical security and privacy concerns, as well as ethical considerations and policies regarding retrieval-augmented generation. Different case studies on knowledge management using summarization techniques, personalized learning in the education sector, and customized chatbots for customer service show the vast potential of retrieval-augmented generation models. This essential guide gives a deep understanding of this transformative technology and how it is revolutionizing how humans interact with machines.
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Author / Editor Details
Sachin Minocha, PhD is an Assistant Professor at Amity University, Uttar Pradesh, India. He has seven patents and more than 15 publications in conference proceedings, book chapters, and refereed journals to his credit. His research areas are machine learning, deep learning, nature-inspired optimization techniques, and hyperspectral images.
Sachin Minocha, PhD is an Assistant Professor at Amity University, Uttar Pradesh, India. He has seven patents and more than 15 publications in conference proceedings, book chapters, and refereed journals to his credit. His research areas are machine learning, deep learning, nature-inspired optimization techniques, and hyperspectral images.
Malathy Sathyamoorthy, PhD is an Assistant Professor in the Department of Information Technology at the KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. She has published more than 25 research papers in international journals, 22 papers in international conferences, two patents, one book, and four book chapters. Her research focuses on wireless sensor networks, networking, security, and machine learning.
Rajesh Kumar Dhanaraj, PhD is a Professor at the Symbiosis International University in Pune, India. He has authored and edited more than 50 books on various cutting-edge technologies and more than 115 articles in international journals and conferences, and holds 22 patents. His research interests encompass machine learning, cyber-physical systems, and wireless sensor networks.
Mayank Kumar Goyal, PhD is an Associate Professor in the Department of Computer Science and Engineering at Sharda University. He has an extensive academic and research background, with more than 60 research papers and articles published in reputed international journals and conferences. His research interests span emerging technologies, innovation, artificial intelligence, cybersecurity, fintech, and intellectual property development.
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