Instruction-tuned large language models have shown remarkable performance in aligning generated text with user intentions across various tasks. However, maintaining human-like discourse structure in the generated text remains a challenging research question. In this paper, we propose Instruct-SCTG, a flexible and effective sequential framework that harnesses instruction-tuned language models to generate structurally coherent text in both fine-tuned and zero-shot setups. Our framework generates articles in a section-by-section manner, aligned with the desired human structure using natural language instructions. Furthermore, we introduce a new automatic metric that measures discourse divergence in a fuzzy manner. Extensive experiments on three datasets from representative domains of news and recipes demonstrate the state-of-the-art performance of our framework in imposing discourse structure during text generation, as verified by both automatic and human evaluation. Our code will be available on Github.

翻译：指令微调的大语言模型在多种任务中展现了使生成文本与用户意图对齐的卓越能力。然而，如何在生成文本中保持类似人类的话语结构仍是一个具有挑战性的研究问题。本文提出Instruct-SCTG，一种灵活高效的顺序框架，通过利用指令微调语言模型在微调和零样本场景中生成结构连贯的文本。该框架以逐节方式生成文章，通过自然语言指令与期望的人类结构对齐。此外，我们引入了一种新的自动评估指标，以模糊方式度量话语发散度。在来自新闻和食谱两个代表性领域的三个数据集上开展的广泛实验表明，我们的框架在文本生成中施加话语结构方面达到了最先进的性能，自动评估与人工评估均验证了这一点。我们的代码将在GitHub上开源。