Pith. sign in

REVIEW 9 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2401.17167 v3 pith:QBHUXFWB submitted 2024-01-30 cs.CL

Shijue Huang , Wanjun Zhong , Jianqiao Lu , Qi Zhu , Jiahui Gao , Weiwen Liu , Yutai Hou , Xingshan Zeng
show 5 more authors
Yasheng Wang Lifeng Shang Xin Jiang Ruifeng Xu Qun Liu
This is my paper
classification cs.CL
keywords toolllmsplanningreal-worldultratoolutilizationcomplexscenarios
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions

    cs.CR 2025-03 unverdicted novelty 7.0 of 10

    MCP lifecycle is defined with four phases and 16 activities; a threat taxonomy of 16 scenarios is constructed, validated via case studies, and paired with phase-specific safeguards.

  2. Characterizing Web Search in The Age of Generative AI

    cs.IR 2025-10 conditional novelty 6.0 of 10

    AI search engines vary greatly in how much they rely on web pages versus internal model knowledge, and these differences shift which sources and concepts users see.

  3. Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems

    cs.SE 2025-07 conditional novelty 6.0 of 10

    LLM tool agents fail at parameter filling in five recurring ways; perturbing tool documents and user queries drives most failures, and invented parameter names are tied to the model rather than the input.

  4. MassTool: A Multi-Task Search-Based Tool Retrieval Framework for Large Language Models

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A multi-task retriever that combines tool-usage detection with query-centered graph and search-based modules improves tool retrieval accuracy over prior baselines.

  5. PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes

    cs.CL 2026-06 unverdicted novelty 5.0 of 10

    Presents PEC-Home dataset for elliptical smart-home commands and shows LLMs achieve lower execution accuracy on elliptical inputs than complete commands even with dialogue history access.

  6. Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions

    cs.CV 2025-09 unverdicted novelty 5.0 of 10

    Structured reflection makes error diagnosis and repair an explicit trainable step that improves reliability and reduces redundant calls in tool-using LLM agents.

  7. Teaching a Language Model to Speak the Language of Tools

    cs.IR 2025-06 conditional novelty 5.0 of 10

    LoRA fine-tuning of BgGPT models on a bilingual Bulgarian function-calling dataset yields large gains on a self-built 120-case benchmark while keeping knowledge benchmarks stable.

  8. AIAP: A No-Code Workflow Builder for Non-Experts with Natural Language and Multi-Agent Collaboration

    cs.HC 2025-08 conditional novelty 4.0 of 10

    A no-code workflow builder with hidden multi-agent decomposition yields positive usability scores, but the study does not support the claim of significant improvement.

  9. Toward Efficient Agents: Memory, Tool learning, and Planning

    cs.AI 2026-01 conditional novelty 3.0 of 10

    A survey that organizes efficiency techniques for LLM agents into memory, tool learning, and planning, and consolidates benchmarks and metrics for measuring cost-performance trade-offs.

Pith tools