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Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios

  • Shijue Huang
  • , Wanjun Zhong*
  • , Jianqiao Lu
  • , Qi Zhu
  • , Jiahui Gao
  • , Weiwen Liu
  • , Yutai Hou
  • , Xingshan Zeng
  • , Yasheng Wang
  • , Lifeng Shang
  • , Xin Jiang
  • , Ruifeng Xu*
  • , Qun Liu
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen
  • Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies
  • Huawei Technologies Co., Ltd.
  • The University of Hong Kong
  • Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationThe 62nd Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationFindings of the Association for Computational Linguistics, ACL 2024
EditorsLun-Wei Ku, Andre Martins, Vivek Srikumar
PublisherAssociation for Computational Linguistics (ACL)
Pages4363-4400
Number of pages38
ISBN (Electronic)9798891760998
DOIs
StatePublished - 2024
Externally publishedYes
EventFindings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Hybrid, Bangkok, Thailand
Duration: 11 Aug 202416 Aug 2024

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

ConferenceFindings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
Country/TerritoryThailand
CityHybrid, Bangkok
Period11/08/2416/08/24

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