Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation. This issue is often attributed to exposure bias - the difference between how a model is trained, and how it is used during inference. Denoising diffusion models provide an alternative approach in which a model can revisit and revise its output. However, they can be computationally expensive and prior efforts on text have led to models that produce less fluent output compared to autoregressive models, especially for longer text and paragraphs. In this paper, we propose PLANNER, a model that combines latent semantic diffusion with autoregressive generation, to generate fluent text while exercising global control over paragraphs. The model achieves this by combining an autoregressive "decoding" module with a "planning" module that uses latent diffusion to generate semantic paragraph embeddings in a coarse-to-fine manner. The proposed method is evaluated on various conditional generation tasks, and results on semantic generation, text completion and summarization show its effectiveness in generating high-quality long-form text in an efficient manner.

翻译：自回归文本模型有时会因生成过程中错误累积而产生重复且低质量的输出。这一问题通常归因于暴露偏差——模型训练方式与推理使用方式之间的差异。去噪扩散模型提供了一种替代方法，允许模型反复修正其输出。然而，这类模型计算成本较高，且现有文本领域的努力导致其生成文本的流畅度（尤其是长文本和段落）仍不及自回归模型。本文提出PLANNER模型，该模型融合潜在语义扩散与自回归生成，既能生成流畅文本，又能对段落实现全局控制。具体而言，模型将自回归"解码"模块与"规划"模块相结合，后者通过潜在扩散以由粗到细的方式生成语义段落嵌入。本方法在多种条件生成任务上进行了评估，语义生成、文本补全与摘要的实验结果表明，该方法能高效生成高质量的长文本。