From Principles to Applications: A Comprehensive Survey of Discrete Tokenizers in Generation, Comprehension, Recommendation, and Information Retrieval

2025-02-18

AI Summary

Discrete tokenizers lack a systematic, cross-task survey. Method: We propose the first unified analytical framework covering generation, understanding, recommendation, and information retrieval; introduce a hierarchical decomposition paradigm for tokenizer submodules; establish a cross-task taxonomy; and conduct a horizontal comparison of representative approaches—including VQ-VAE, SoundStream, K-means tokenization, semantic hashing, and cross-modal alignment—through the lenses of information theory, representation learning, and structured modeling. Contribution/Results: We identify three core challenges: semantic alignment, cross-modal generalization, and the efficiency–accuracy trade-off. Furthermore, we deliver a reusable evaluation dimension matrix and an open challenge map, providing both theoretical foundations and practical guidelines for designing next-generation tokenizers that are robust, interpretable, and cross-modal.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Discrete tokenizers have emerged as indispensable components in modern machine learning systems, particularly within the context of autoregressive modeling and large language models (LLMs). These tokenizers serve as the critical interface that transforms raw, unstructured data from diverse modalities into discrete tokens, enabling LLMs to operate effectively across a wide range of tasks. Despite their central role in generation, comprehension, and recommendation systems, a comprehensive survey dedicated to discrete tokenizers remains conspicuously absent in the literature. This paper addresses this gap by providing a systematic review of the design principles, applications, and challenges of discrete tokenizers. We begin by dissecting the sub-modules of tokenizers and systematically demonstrate their internal mechanisms to provide a comprehensive understanding of their functionality and design. Building on this foundation, we synthesize state-of-the-art methods, categorizing them into multimodal generation and comprehension tasks, and semantic tokens for personalized recommendations. Furthermore, we critically analyze the limitations of existing tokenizers and outline promising directions for future research. By presenting a unified framework for understanding discrete tokenizers, this survey aims to guide researchers and practitioners in addressing open challenges and advancing the field, ultimately contributing to the development of more robust and versatile AI systems.

Problem

• Survey of discrete tokenizers in AI systems
• Review design, applications, and challenges of tokenizers
• Guide future research on tokenizers for AI advancement

Innovation

• Discrete tokenizers transform unstructured data
• Systematic review of tokenizer design principles
• Categorize methods for multimodal generation tasks