Controlled Text Generation for Large Language Model with Dynamic
  Attribute Graphs
Xun Liang Hanyu Wang Shichao Song Mengting Hu Xunzhi Wang Zhiyu Li Feiyu Xiong Bo Tang
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.