| --- |
| 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} |
| } |
| ``` |
|
|
