Abstract
In recent years, natural language generative models have shown outstanding performance in text generation tasks. However, when facing specific tasks or particular requirements, they may exhibit poor performance or require adjustments that demand large amounts of additional data. This work introduces PAGE (Prompt Augmentation for text Generation Enhancement), a framework designed to assist these models through the use of simple auxiliary modules. These modules, lightweight models such as classifiers or extractors, provide inferences from the input text. The output of these auxiliaries is then used to construct an enriched input that improves the quality and controllability of the generation. Unlike other generation-assistance approaches, PAGE does not require auxiliary generative models; instead, it proposes a simpler, modular architecture that is easy to adapt to different tasks. This paper presents the proposal, its components and architecture, and reports a proof of concept in the domain of requirements engineering, where an auxiliary module with a classifier is used to improve the quality of software requirements generation.
Abstract (translated)
近年来，自然语言生成模型在文本生成任务中表现出色。然而，在面对特定任务或具体需求时，这些模型可能会表现不佳或需要大量额外数据进行调整以改进性能。这项工作引入了PAGE（Prompt Augmentation for text Generation Enhancement），这是一个通过使用简单辅助模块来帮助这些模型的框架。这些轻量级模型（如分类器或提取器）会从输入文本中提供推断结果。然后，将这些辅助模块的输出用于构建增强后的输入，以提高生成的质量和可控性。与其它生成辅助方法不同的是，PAGE不需要额外的生成模型；相反，它提出了一种简单、模块化的架构，易于适应不同的任务需求。 本文介绍了该框架的提案及其组件和架构，并在软件需求工程领域展示了概念验证案例，在此案例中，使用了一个带有分类器的辅助模块来提高软件需求生成的质量。
URL
https://arxiv.org/abs/2510.13880
