最新综述！扩散语言模型全面盘点~

Core Viewpoint - The article discusses the competition between two major paradigms in generative AI: Diffusion Models and Autoregressive (AR) Models, highlighting the emergence of Diffusion Language Models (DLMs) as a potential breakthrough in the field of large language models [2][3]. Group 1: DLM Advantages Over AR Models - DLMs offer parallel generation capabilities, significantly improving inference speed by achieving a tenfold increase compared to AR models, which are limited by token-level serial processing [11][12]. - DLMs utilize bidirectional context, enhancing language understanding and generation control, allowing for finer adjustments in output characteristics such as sentiment and structure [12][14]. - The iterative denoising mechanism of DLMs allows for corrections during the generation process, reducing the accumulation of early errors, which is a limitation in AR models [13]. - DLMs are naturally suited for multimodal applications, enabling the integration of text and visual data without the need for separate modules, thus enhancing the quality of joint generation tasks [14]. Group 2: Technical Landscape of DLMs - DLMs are categorized into three paradigms: Continuous Space DLMs, Discrete Space DLMs, and Hybrid AR-DLMs, each with distinct advantages and applications [15][20]. - Continuous Space DLMs leverage established diffusion techniques from image models but may suffer from semantic loss during the embedding process [20]. - Discrete Space DLMs operate directly on token levels, maintaining semantic integrity and simplifying the inference process, making them the mainstream approach in large parameter models [21]. - Hybrid AR-DLMs combine the strengths of AR models and DLMs, balancing efficiency and quality for tasks requiring high coherence [22]. Group 3: Training and Inference Optimization - DLMs utilize transfer learning to reduce training costs, with methods such as initializing from AR models or image diffusion models, significantly lowering data requirements [30][31]. - The article outlines three main directions for inference optimization: parallel decoding, masking strategies, and efficiency technologies, all aimed at enhancing speed and quality [35][38]. - Techniques like confidence-aware decoding and dynamic masking are highlighted as key innovations to improve the quality of generated outputs while maintaining high inference speeds [38][39]. Group 4: Multimodal Applications and Industry Impact - DLMs are increasingly applied in multimodal contexts, allowing for unified processing of text and visual data, which enhances capabilities in tasks like visual reasoning and joint content creation [44]. - The article presents various case studies demonstrating DLMs' effectiveness in high-value vertical applications, such as code generation and computational biology, showcasing their potential in real-world scenarios [46]. - DLMs are positioned as a transformative technology in industries, with applications ranging from real-time code generation to complex molecular design, indicating their broad utility [46][47]. Group 5: Challenges and Future Directions - The article identifies key challenges facing DLMs, including the trade-off between parallelism and performance, infrastructure limitations, and scalability issues compared to AR models [49][53]. - Future research directions are proposed, focusing on improving training objectives, building dedicated toolchains, and enhancing long-sequence processing capabilities [54][56].