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.

翻译：基于领域数据集训练语言模型的方法在生成科学论文摘要任务上取得了显著成就。然而，此类模型面临泛化能力不足和训练成本高昂的问题。采用大型语言模型（LLM）解决论文摘要生成任务可节省模型训练成本，但受限于LLM的幻觉问题，通常需要借助思维图（GoT）等多轮查询提示方法提升结果可靠性，这同时带来了额外的推理成本。本文提出动态思维图（DGoT），该方法不仅继承了现有GoT提示方法的优势，还能根据数据特征动态调整图结构，同时降低模型推理成本。实验结果表明，在摘要生成任务中，本方法的成本效益仅为其他多轮查询提示方法的43.7%至56.4%。我们的代码已开源至https://github.com/JayceNing/DGoT。