Optimizing Retrieval: From Tokenization To Vector Quantization - Softcover

Inhaltsangabe

"Optimizing Retrieval: From Tokenization to Vector Quantization"

This book provides a deep dive into the core techniques that underpin modern information retrieval systems. It guides readers through the crucial steps, starting with the fundamental process of tokenization – breaking down text into meaningful units. From there, the book explores how these tokens are transformed into numerical representations, a critical step for efficient processing.

The core of the book lies in vector quantization, a powerful technique that compresses and represents high-dimensional data (like text) into lower-dimensional spaces while preserving essential information. This enables faster search, reduced storage requirements, and improved retrieval accuracy.1

Key Topics Covered:

• Tokenization Strategies: Exploring various approaches, including word-level, subword-level (like byte-pair encoding), and character-level tokenization.
• Text Embedding Techniques: Delving into methods like Word2Vec, GloVe, and more recently, Transformer-based models like BERT, which capture semantic relationships between words.2
• Vector Quantization Algorithms: Examining different approaches, such as k-means, product quantization, and hierarchical vector quantization, and their applications in information retrieval.
• Retrieval Models: Exploring how vector quantization is integrated into various retrieval models, including nearest neighbor search, approximate nearest neighbor search, and retrieval augmented generation.
• Practical Applications: Discussing real-world applications of these techniques, such as search engines, recommendation systems, and question answering systems.

"Optimizing Retrieval: From Tokenization to Vector Quantization" is a valuable resource for researchers, practitioners, and students interested in the cutting-edge techniques driving advancements in information retrieval. It provides a comprehensive understanding of the key concepts and their practical implications, empowering readers to build and optimize high-performance retrieval systems.

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