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. We have also created a GitHub repository for indexing relevant papers and open resources available at https://github.com/zhanglingzhe0820/Awesome-Parallel-Text-Generation.

翻译：随着文本生成成为现代大语言模型（LLM）的核心能力，它支撑着广泛的下游应用。然而，现有大多数LLM依赖于自回归（AR）生成方式，即基于先前生成的上下文逐个生成词元——由于该过程固有的顺序性，导致生成速度受限。为应对这一挑战，越来越多的研究者开始探索并行文本生成——这是一类旨在打破逐词元生成瓶颈、提升推理效率的广泛技术。尽管关注度日益增长，但对于哪些具体技术构成并行文本生成以及它们如何提升推理性能，仍缺乏全面的分析。为弥合这一差距，本文对并行文本生成方法进行了系统性综述。我们将现有方法划分为基于AR与基于非AR的两大范式，并对每类范式中的核心技术进行了详细剖析。基于此分类体系，我们评估了它们在速度、质量和效率方面的理论权衡，并探讨了它们与替代加速策略结合及比较的潜力。最后，基于研究发现，我们重点阐述了最新进展，指出了开放挑战，并展望了并行文本生成领域未来研究的潜在方向。我们还创建了一个GitHub仓库，用于索引相关论文和开放资源，访问地址为 https://github.com/zhanglingzhe0820/Awesome-Parallel-Text-Generation。