@inproceedings{guo-etal-2025-stabletoolbench,
    title = "{S}table{T}ool{B}ench-{M}irror{API}: Modeling Tool Environments as Mirrors of 7,000+ Real-World {API}s",
    author = "Guo, Zhicheng  and
      Cheng, Sijie  and
      Niu, Yuchen  and
      Wang, Hao  and
      Zhou, Sicheng  and
      Huang, Wenbing  and
      Liu, Yang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.273/",
    pages = "5247--5270",
    ISBN = "979-8-89176-256-5",
    abstract = "The rapid advancement of large language models (LLMs) has spurred significant interest in tool learning, where LLMs are augmented with external tools to tackle complex tasks. However, existing tool environments face challenges in balancing stability, scale, and realism, particularly for benchmarking purposes. To address this, we propose MirrorAPI, a novel framework that trains specialized LLMs to accurately simulate real API responses, effectively acting as ``mirrors'' to tool environments. Using a comprehensive dataset of request-response pairs from 7,000+ APIs, we employ supervised fine-tuning and chain-of-thought reasoning to enhance simulation fidelity. MirrorAPI achieves superior accuracy and stability compared to state-of-the-art methods, as demonstrated by its performance on the newly constructed MirrorAPI-Bench and its integration into StableToolBench."
}