@inproceedings{huang-etal-2026-fintoolsyn,
    title = "{F}in{T}ool{S}yn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval",
    author = "Huang, Caishuang  and
      Qiao, Yang  and
      Zhang, Rongyu  and
      Ye, Junjie  and
      Lu, Pu  and
      Wuwenxi  and
      Zhou, Meng  and
      Du, Xiku  and
      Zhang, Qi  and
      Gui, Tao  and
      Huang, Xuanjing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.746/",
    pages = "15153--15196",
    ISBN = "979-8-89176-395-1",
    abstract = "Tool-use capabilities are vital for Large Language Models (LLMs) in finance, a domain characterized by massive investment targets and data-intensive inquiries. However, existing data synthesis methods typically rely on a reverse synthesis paradigm, generating user queries from pre-sampled tools. This approach inevitably introduces artificial explicitness, yielding queries that fail to capture the implicit, event-driven nature of real-world needs. Moreover, its reliance on static tool sets overlooks the dynamic retrieval process required to navigate massive tool spaces. To address these challenges, we introduce $\textit{FinToolSyn}$, a forward synthesis framework designed to generate high-quality financial dialogues. Progressing from persona instruction and atomic tool synthesis to dynamic retrieval dialogue generation, our pipeline constructs a repository of 43,066 tools and synthesizes over 148k dialogue instances, incorporating dynamic retrieval to emulate the noisy candidate sets typical of massive tool spaces. We also establish a dedicated benchmark to evaluate tool-calling capabilities in realistic financial scenarios. Extensive experiments demonstrate that models trained on FinToolSyn achieve a 21.06{\%} improvement, providing a robust foundation for tool learning in financial scenarios."
}