The method of training language models based on domain datasets has obtained
significant achievements in the task of generating scientific paper abstracts.
However, such models face problems of generalization and expensive training
costs. The use of large language models (LLMs) to solve the task of generating
paper abstracts saves the cost of model training. However, due to the
hallucination problem of LLM, it is often necessary to improve the reliability
of the results through multi-round query prompt approach such as Graph of
Thoughts (GoT), which also brings additional reasoning costs. In this paper, we
propose a Dynamic Graph of Thought (DGoT). It not only inherits the advantages
of the existing GoT prompt approach, but also dynamically adjust the graph
structure according to data characteristics while reducing model reasoning
cost. Experimental results show that our method's cost-effectiveness in
abstract generation tasks is only 43.7% to 56.4% of other multi-round query
prompt approaches. Our code is available at this https URL

我们提出了一种动态思维图 (DGoT) 的方法，它在减少模型推理成本的同时，不仅继承了现有 GoT 提示方法的优点，还根据数据特征动态调整图结构。实验结果表明，我们的方法在摘要生成任务中的性价比仅为其他多轮查询提示方法的 43.7% 至 56.4%。