TY - GEN
T1 - Planning, Creation, Usage
T2 - Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
AU - Huang, Shijue
AU - Zhong, Wanjun
AU - Lu, Jianqiao
AU - Zhu, Qi
AU - Gao, Jiahui
AU - Liu, Weiwen
AU - Hou, Yutai
AU - Zeng, Xingshan
AU - Wang, Yasheng
AU - Shang, Lifeng
AU - Jiang, Xin
AU - Xu, Ruifeng
AU - Liu, Qun
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools. However, existing benchmarks typically focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization. To address this issue, we present UltraTool, a novel benchmark designed to improve and evaluate LLMs' ability in tool utilization within real-world scenarios. UltraTool focuses on the entire process of using tools - from planning and creating to applying them in complex tasks. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage and simplifies the task solving by mapping out the intermediate steps. Thus, unlike previous work, it eliminates the restriction of pre-defined toolset. Through extensive experiments on various LLMs, we offer novel insights into the evaluation of capabilities of LLMs in tool utilization, thereby contributing a fresh perspective to this rapidly evolving field. The benchmark is publicly available at https://github.com/JoeYing1019/UltraTool.
AB - The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools. However, existing benchmarks typically focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization. To address this issue, we present UltraTool, a novel benchmark designed to improve and evaluate LLMs' ability in tool utilization within real-world scenarios. UltraTool focuses on the entire process of using tools - from planning and creating to applying them in complex tasks. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage and simplifies the task solving by mapping out the intermediate steps. Thus, unlike previous work, it eliminates the restriction of pre-defined toolset. Through extensive experiments on various LLMs, we offer novel insights into the evaluation of capabilities of LLMs in tool utilization, thereby contributing a fresh perspective to this rapidly evolving field. The benchmark is publicly available at https://github.com/JoeYing1019/UltraTool.
UR - https://www.scopus.com/pages/publications/85205305094
U2 - 10.18653/v1/2024.findings-acl.259
DO - 10.18653/v1/2024.findings-acl.259
M3 - 会议稿件
AN - SCOPUS:85205305094
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 4363
EP - 4400
BT - The 62nd Annual Meeting of the Association for Computational Linguistics
A2 - Ku, Lun-Wei
A2 - Martins, Andre
A2 - Srikumar, Vivek
PB - Association for Computational Linguistics (ACL)
Y2 - 11 August 2024 through 16 August 2024
ER -