README.md

---
license: apache-2.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - multi-hop-qa
  - tool-calling
  - agent
  - data-synthesis
pretty_name: ToolForge Source QA
size_categories:
  - 100K<n<1M
configs:
  - config_name: bridge_hp
    data_files: original_data/HotpotQA/bridge_hp.parquet
  - config_name: comparison_hp
    data_files: original_data/HotpotQA/comparison_hp.parquet
  - config_name: bridge_comparison_wiki
    data_files: original_data/2WikiMultihopQA/bridge_comparison_wiki.parquet
  - config_name: comparison_wiki
    data_files: original_data/2WikiMultihopQA/comparison_wiki.parquet
  - config_name: compositional_wiki
    data_files: original_data/2WikiMultihopQA/compositional_wiki.parquet
  - config_name: inference_wiki
    data_files: original_data/2WikiMultihopQA/inference_wiki.parquet
---

# ToolForge — Source QA

The multi-hop question answering corpora that **[ToolForge](https://github.com/Buycar-arb/ToolForge)**
takes as input, in the exact slices used in the paper.

📄 **Paper** — [arXiv:2512.16149](https://arxiv.org/abs/2512.16149)
💻 **Code** — [github.com/Buycar-arb/ToolForge](https://github.com/Buycar-arb/ToolForge)

```bash
git clone https://github.com/Buycar-arb/ToolForge.git && cd ToolForge
pip install -e ".[all]"
python download_data.py
```

## Contents

257,901 questions across six corpora, drawn from HotpotQA and 2WikiMultihopQA.

| corpus | questions | size | question shape |
|---|---:|---:|---|
| `HotpotQA/bridge_hp` | 72,991 | 246.5 MB | bridge: the answer to hop 1 identifies the subject of hop 2 |
| `HotpotQA/comparison_hp` | 17,456 | 52.5 MB | comparison: "which of X and Y…" |
| `2WikiMultihopQA/compositional_wiki` | 76,481 | 134.9 MB | compositional: "the Z of the Y of X" |
| `2WikiMultihopQA/comparison_wiki` | 51,963 | 92.4 MB | comparison |
| `2WikiMultihopQA/bridge_comparison_wiki` | 34,631 | 70.8 MB | bridge *and* comparison combined |
| `2WikiMultihopQA/inference_wiki` | 4,379 | 9.7 MB | inference over family relations |

## Record schema

```json
{
  "_id": "5a8b57f25542995d1e6f1371",
  "question": "Were Scott Derrickson and Ed Wood of the same nationality?",
  "answer": "yes",
  "type": "comparison",
  "context": [
    ["Scott Derrickson", ["Scott Derrickson (born 1966) is an American director."]],
    ["Ed Wood", ["Edward Davis Wood Jr. was an American director and screenwriter."]]
  ],
  "supporting_facts": [["Scott Derrickson", 0], ["Ed Wood", 0]]
}
```

`supporting_facts` is what makes the pipeline work: it splits `context` into the
passages that contain the answer and everything else. ToolForge treats the first
as the evidence a tool call should surface, and runs BM25 over the second to
produce realistic distractors — which is how a *failed* tool call in the
generated data can be genuinely, plausibly unhelpful.

Nested columns are stored as JSON strings so they fit in Parquet. Convert to the
JSONL the pipeline reads with:

```bash
toolforge convert to-jsonl original_data/HotpotQA
```

## Which corpus for which dialogue shape

ToolForge generates 29 dialogue cases in four families, and a corpus can only
produce the families its questions are shaped for. Measured by sampling and
labelling each corpus:

| corpus | → A | → B | → C | → D | good for |
|---|---:|---:|---:|---:|---|
| `inference_wiki` | 93% | 7% | — | — | **A** |
| `comparison_wiki` | — | 100% | — | — | **B** |
| `comparison_hp` | 16% | 84% | — | — | A, **B** |
| `compositional_wiki` | 13% | — | 83% | 3% | **C** |
| `bridge_hp` | 76% | 4% | 20% | — | **A**, C |
| `bridge_comparison_wiki` | 4% | 44% | 36% | 16% | **B, C, D** |

`bridge_comparison_wiki` is the only corpus that reliably yields **D** — label
roughly six times as much of it to get a comparable number of D samples.
Details in [`docs/choosing-source-data.md`](https://github.com/Buycar-arb/ToolForge/blob/main/docs/choosing-source-data.md).

## Citation

```bibtex
@article{chen2025toolforge,
  title={ToolForge: A Data Synthesis Pipeline for Multi-Hop Search without Real-World APIs},
  author={Chen, Hao and Hu, Zhexin and Chai, Jiajun and Yang, Haocheng and He, Hang and Wang, Xiaohan and Lin, Wei and Wang, Luhang and Yin, Guojun and others},
  journal={arXiv preprint arXiv:2512.16149},
  year={2025}
}
```

Please also cite the original datasets:
[HotpotQA](https://arxiv.org/abs/1809.09600) ·
[2WikiMultihopQA](https://arxiv.org/abs/2011.01060)