Controlled Text Generation (CTG) aims to produce texts that exhibit specific
desired attributes. In this study, we introduce a pluggable CTG framework for
Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled
text generation (DATG). This framework utilizes an attribute scorer to evaluate
the attributes of sentences generated by LLMs and constructs dynamic attribute
graphs. DATG modulates the occurrence of key attribute words and key
anti-attribute words, achieving effective attribute control without
compromising the original capabilities of the model. We conduct experiments
across four datasets in two tasks: toxicity mitigation and sentiment
transformation, employing five LLMs as foundational models. Our findings
highlight a remarkable enhancement in control accuracy, achieving a peak
improvement of 19.29% over baseline methods in the most favorable task across
four datasets. Additionally, we observe a significant decrease in perplexity,
markedly improving text fluency.

控制性文本生成旨在生成具有特定期望属性的文本。本研究中，我们介绍了一种用于大型语言模型的可插拔的控制性文本生成框架（DATG），名为动态属性图控制性文本生成。该框架利用属性评分器评估语言模型生成的句子的属性，并构建动态属性图。DATG 通过调节关键属性词和关键反属性词的出现频率，在不损害模型原始能力的情况下实现了有效的属性控制。我们在两个任务的四个数据集上进行实验：毒性缓解和情感转换，并使用五种语言模型作为基础模型。我们的发现突出了控制精度的显著提高，在四个数据集中最优任务上改进了 19.29％。此外，我们观察到困惑度显著减少，文本流畅性得到显著改善。