@inproceedings{zhu-etal-2025-divide,
    title = "Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation",
    author = "Zhu, Dongsheng  and
      Shi, Weixian  and
      Shi, Zhengliang  and
      Ren, Zhaochun  and
      Wang, Shuaiqiang  and
      Yan, Lingyong  and
      Yin, Dawei",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1401/",
    pages = "28859--28875",
    ISBN = "979-8-89176-251-0",
    abstract = "While Large Language Models (LLMs) demonstrate remarkable capabilities, their ability to autonomously execute complex real-world tasks remains limited. Accordingly, tool learning has emerged to enable LLMs to effectively leverage external tools to extend their capabilities. Current tool-learning paradigms like CoT/ReAct employ sequential tool invocation but suffer from constrained perception and inadequate task planning. Alternative approaches using search-based decision trees incur substantial computational overhead. To address these limitations, we propose DTA-Llama (Divide-Then-Aggregate Llama), a novel parallel tool invocation framework featuring: (1) A Directed Acyclic Graph (DAG) structure that transformed from traditional tree-based tool search paths, enabling parallel execution and contributing high-quality training data; (2) A process-thread-inspired inference mechanism that iteratively decomposes tasks into parallel tool-using subtasks while aggregating results for subsequent decisions. Experimental results show that our approach substantially enhances task performance while reducing token consumption and inference time. Llama2-7B, using our method, is comparable to the official parallel function calling method of GPT-3.5. The relevant code, dataset, and model weights are available at https://corn0205.github.io/."
}