StableToolBench-MirrorAPI: Modeling Tool Environments as Mirrors of 7,000+ Real-World APIs
Zhicheng Guo, Sijie Cheng, Yuchen Niu, Hao Wang, Sicheng Zhou, Wenbing Huang, Yang Liu
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.
BibTeX
@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://aclanthology.org/2025.findings-acl.273/",
doi = "10.18653/v1/2025.findings-acl.273",
pages = "5247--5270",
ISBN = "979-8-89176-256-5"
}