File size: 2,970 Bytes
153066c 4a6d05f 153066c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | ---
configs:
- config_name: single_hop
default: true
data_files:
- split: train
path: single_hop/train-*.parquet
- split: test
path: single_hop/test-*.parquet
- config_name: multi_hop
data_files:
- split: train
path: multi_hop/train-*.parquet
- split: test
path: multi_hop/test-*.parquet
license: mit
task_categories:
- question-answering
language:
- en
tags:
- tool-use
- agents
- llm
- benchmark
pretty_name: When2Tool
size_categories:
- 1K<n<10K
---
# When2Tool
Benchmark dataset for **"LLM Agents Already Know When to Call Tools — Even Without Reasoning"** ([arXiv:2605.09252](https://arxiv.org/abs/2605.09252)).
## Overview
When2Tool is a benchmark of 18 environments designed to study *when* LLM agents should call tools. Tasks range from trivially solvable without tools to impossible without them, across three categories of tool necessity:
- **Computational Scale** (5 envs): Calculator, Statistics, Counting, Matrix, Prime
- **Knowledge Boundaries** (5 envs): Retriever, Historical Year, Game Rule, Hash, Decoding
- **Execution Reliability** (5 envs): List Manipulation, DateTime, Code Executor, Schedule, Regex Match
- **Multi-hop** (3 envs): Calculator, Retriever, Code Executor (3-step chains)
Each environment has three difficulty levels (easy, medium, hard) that create a clear decision boundary between tool-necessary and tool-unnecessary tasks.
## Dataset Structure
### Configs
- `single_hop`: 15 single-hop environments (900 train / 2,250 test)
- `multi_hop`: 3 multi-hop environments with 3-step chains (180 train / 450 test)
### Fields
| Field | Type | Description |
|-------|------|-------------|
| `id` | int | Unique task identifier |
| `difficulty` | str | `easy`, `medium`, or `hard` |
| `multi_step` | bool | Whether the task requires multiple tool calls |
| `instruction` | str | The task instruction given to the agent |
| `env_name` | str | Environment name (e.g., `CalculatorEnv`) |
| `tools` | str (JSON) | Available tools for this environment |
| `parameters` | str (JSON) | Environment parameters (e.g., corpus for retriever) |
| `answer` | str | Expected final answer |
| `steps` | str (JSON) | Intermediate steps for multi-hop tasks |
| `tags` | str (JSON) | Environment and task type tags |
### Loading
```python
from datasets import load_dataset
# Single-hop tasks
ds = load_dataset("Trustworthy-ML-Lab/When2Tool", "single_hop")
# Multi-hop tasks
ds_mh = load_dataset("Trustworthy-ML-Lab/When2Tool", "multi_hop")
# Access a sample
print(ds["test"][0]["instruction"])
print(ds["test"][0]["env_name"])
```
## Citation
```bibtex
@article{sun2026when2tool,
title={LLM Agents Already Know When to Call Tools -- Even Without Reasoning},
author={Sun, Chung-En and Liu, Linbo and Yan, Ge and Wang, Zimo and Weng, Tsui-Wei},
journal={arXiv preprint arXiv:2605.09252},
year={2026},
url={https://arxiv.org/abs/2605.09252}
}
```
|