@inproceedings{ning-etal-2024-dgot,
    title = "{DG}o{T}: Dynamic Graph of Thoughts for Scientific Abstract Generation",
    author = "Ning, Xinyu  and
      Zhao, Yutong  and
      Liu, Yitong  and
      Yang, Hongwen",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.lrec-main.433/",
    pages = "4832--4846",
    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."
}