Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
CoRR(2024)
摘要
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
four datasets. Additionally, we observe a significant decrease in perplexity,
markedly improving text fluency.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要