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
摘要
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.
问 Lune
尽管问,回答会引用原文。
Lune 会读完全文和相关的 Paper,每个回答都附上所依据的原文段落。
引用它的顶会 Paper18
- Can Graph Learning Improve Planning in LLM-based Agents?Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song 等NeurIPS 2024 · 67 次引用
- UltraHorizon: Benchmarking LLM-Agent Capabilities in Ultra Long-Horizon ScenariosHaotian Luo, Huaisong Zhang, Xuelin Zhang, Haoyu Wang 等ICML 2026 · 21 次引用
- Benchmarking LLM Tool-Use in the WildPeijie Yu, Wei Liu, Yifan Yang, Jinjian Li 等ICLR 2026 · 20 次引用
- CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use AgentsJiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong 等ACL 2026 · 19 次引用
- Tools are under-documented: Simple Document Expansion Boosts Tool RetrievalXuan Lu, Haohang Huang, Rui Meng, Yaohui Jin 等ICLR 2026 · 16 次引用
它引用的 Paper6
- ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool EmbeddingsShibo Hao, Tianyang Liu, Zhen Wang, Zhiting HuNeurIPS 2023 · 315 次引用
- Large Language Models as Tool MakersTianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen 等ICLR 2024 · 283 次引用
- MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to UseYue Huang, Jiawen Shi, Yuan Li, Chenrui Fan 等ICLR 2024 · 188 次引用
- API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMsMinghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song 等EMNLP 2023 · 72 次引用
- AlignBench: Benchmarking Chinese Alignment of Large Language ModelsXiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang 等ACL 2024 · 9 次引用
相关 Paper
- Beyond Itinerary Planning - A Real-World Benchmark for Multi-Turn and Tool-Using Travel TasksXiang Cheng, Yulan Hu, Xiangwen Zhang, Lu Xu 等ACL 2026 · 4 次引用
- TravelPlanner: A Benchmark for Real-World Planning with Language AgentsJian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu 等ICML 2024 · 376 次引用
- ACEBench: A Comprehensive Evaluation of LLM Tool UsageChen Chen, Xinlong Hao, Weiwen Liu, Xu Huang 等EMNLP Findings 2025 · 1 次引用
- TRAJECT-Bench: A Trajectory-Aware Benchmark for Evaluating Agentic Tool UsePengfei He, Zhenwei Dai, Bing He, Hui Liu 等ICLR 2026 · 46 次引用
- T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by StepZehui Chen, Weihua Du, Wenwei Zhang, Kuikun Liu 等ACL 2024 · 7 次引用
