ACL Findings2024
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
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.
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Cited by top-tier papers18
- Can Graph Learning Improve Planning in LLM-based Agents?Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song et al.NeurIPS 2024 · 67 citations
- UltraHorizon: Benchmarking LLM-Agent Capabilities in Ultra Long-Horizon ScenariosHaotian Luo, Huaisong Zhang, Xuelin Zhang, Haoyu Wang et al.ICML 2026 · 21 citations
- Benchmarking LLM Tool-Use in the WildPeijie Yu, Wei Liu, Yifan Yang, Jinjian Li et al.ICLR 2026 · 20 citations
- CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use AgentsJiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong et al.ACL 2026 · 19 citations
- Tools are under-documented: Simple Document Expansion Boosts Tool RetrievalXuan Lu, Haohang Huang, Rui Meng, Yaohui Jin et al.ICLR 2026 · 16 citations
Builds on6
- ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool EmbeddingsShibo Hao, Tianyang Liu, Zhen Wang, Zhiting HuNeurIPS 2023 · 315 citations
- Large Language Models as Tool MakersTianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen et al.ICLR 2024 · 283 citations
- MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to UseYue Huang, Jiawen Shi, Yuan Li, Chenrui Fan et al.ICLR 2024 · 188 citations
- API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMsMinghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song et al.EMNLP 2023 · 72 citations
- AlignBench: Benchmarking Chinese Alignment of Large Language ModelsXiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang et al.ACL 2024 · 9 citations
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