DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation
Xinyu Ning Yutong Zhao Yitong Liu Hongwen Yang
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 https://github.com/JayceNing/DGoT.