Enhancing Tool Calling in LLMs with the International Tool Calling Dataset
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
Tool calling allows large language models (LLMs) to interact with external systems like APIs, enabling applications in customer support, data analysis, and dynamic content generation. While recent benchmarks have advanced tool-use research, they suffer from key limitations, including reliance on simulated or restricted APIs, limited reproducibility, and a lack of cultural and geographic diversity. To address these gaps, we introduce International Tool Calling (ITC), a large-scale, multilingual benchmark designed for realistic, globally distributed tool-calling scenarios. ITC includes 3,571 real APIs and 17,540 tool calling tasks across 20 categories and 40 countries. Experiments reveal substantial performance gaps between open- and closed-source LLMs, while fine-tuning on ITC yields significant improvements, particularly for non-English queries, enhancing cross-lingual generalization, reasoning consistency, and robustness to out-of-domain tools. ITC provides a valuable benchmark for advancing LLM robustness and performance in complex, multi-tool, and international scenarios. Dataset: https://anonymous.4open.science/r/International-Tool-Calling-ITC-dataset-FAF4/.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 14 of its 23 references that are in the map. First posted on arXiv 21 Jan 2026.
Builds on
14 of 23 references.
Closest ancestors · 11
Same direction, newest first
- 2025Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling30 Sep
- 2025CONFETTI: Conversational Function-Calling Evaluation Through Turn-Level Interactions2 Jun
- 2025ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use5 Jan
- 2024APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets26 Jun
- 2024Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark14 May
- 2024Planning and Editing What You Retrieve for Enhanced Tool Learning30 Mar
- 2024ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages16 Feb
- 2024RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning16 Jan
- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
- 2023Gorilla: Large Language Model Connected with Massive APIs24 May
- 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb
Other references · 3
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2024SciAgent: Tool-augmented Language Models for Scientific Reasoningcited by 69
- 2024Evaluating Tool-Augmented Agents in Remote Sensing Platformscited by 17
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Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
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- 2025Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
- 2025Live API-Bench: 2500+ Live APIs for Testing Multi-Step Tool Calling
- 2024Tool Learning with Large Language Models: A Survey
- 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
- 2026Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models
- 2025OR-Toolformer: Modeling and Solving Operations Research Problems with Tool Augmented Large Language Models