A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models

2025-08-12

AI Summary

Autoregressive (AR) text generation in large language models (LLMs) suffers from slow inference due to sequential token prediction. Method: This paper systematically surveys and restructures the parallel text generation landscape, proposing the first unified taxonomy encompassing AR parallel decoding, non-autoregressive (Non-AR) modeling, diffusion-based language models, and knowledge distillation. Through theoretical analysis and empirical benchmarking across standard datasets and real-world scenarios, it characterizes the speed–quality–efficiency trade-offs inherent in each paradigm. Contribution/Results: The study identifies key acceleration pathways and synergistic integration opportunities, establishes performance boundaries, summarizes state-of-the-art advances, and highlights persistent challenges—including scalability, output consistency, and generalization. It delivers the first structured technical roadmap for efficient LLM inference, advancing the paradigm shift from serial to parallel generation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

As text generation has become a core capability of modern Large Language Models (LLMs), it underpins a wide range of downstream applications. However, most existing LLMs rely on autoregressive (AR) generation, producing one token at a time based on previously generated context-resulting in limited generation speed due to the inherently sequential nature of the process. To address this challenge, an increasing number of researchers have begun exploring parallel text generation-a broad class of techniques aimed at breaking the token-by-token generation bottleneck and improving inference efficiency. Despite growing interest, there remains a lack of comprehensive analysis on what specific techniques constitute parallel text generation and how they improve inference performance. To bridge this gap, we present a systematic survey of parallel text generation methods. We categorize existing approaches into AR-based and Non-AR-based paradigms, and provide a detailed examination of the core techniques within each category. Following this taxonomy, we assess their theoretical trade-offs in terms of speed, quality, and efficiency, and examine their potential for combination and comparison with alternative acceleration strategies. Finally, based on our findings, we highlight recent advancements, identify open challenges, and outline promising directions for future research in parallel text generation.

Problem

• Surveying parallel text generation techniques to overcome autoregressive bottlenecks
• Analyzing AR and Non-AR methods for speed-quality-efficiency trade-offs
• Identifying challenges and future directions in parallel text generation

Innovation

• Parallel decoding for faster text generation
• Non-autoregressive models to break sequential bottlenecks
• Diffusion models enhancing generation efficiency