DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation

• URL: http://arxiv.org/abs/2403.17491v1
• Date: Tue, 26 Mar 2024 08:47:23 GMT
• Title: DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation
• Authors: Xinyu Ning, Yutong Zhao, Yitong Liu, Hongwen Yang,
• Abstract summary: We propose a Dynamic Graph of Thought (DGoT) to solve the task of generating scientific paper abstracts. Our method's cost-effectiveness in abstract generation tasks is only 43.7% to 56.4% of other multi-round query prompt approaches.
• Score: 4.404836880890741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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