Datasets:
File size: 5,400 Bytes
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language:
- en
task_categories:
- question-answering
tags:
- procedural-reasoning
- rule-reasoning
- retrieval
- synthetic-data
- large-language-models
pretty_name: RuleWorld
size_categories:
- 1M<n<10M
---
# RuleWorld
RuleWorld is a large-scale benchmark for evaluating whether language models can retrieve and apply a shared repository of externally provided procedural rules. Its rules are abstract, globally reusable, and intentionally non-commonsense, so a model cannot answer reliably from world knowledge alone. Each rule is supplied in aligned natural-language (NL) and first-order logic (FOL) forms.
The benchmark covers three reasoning settings: Single-Rule QA, Parallel Multi-Rule QA, and Multi-Hop QA. Parallel questions combine independent sub-questions, while multi-hop questions require sequential rule application.
<table>
<tr>
<td width="70%" align="center"><img src="assets/benchmark.png" alt="RuleWorld overview" width="100%"></td>
<td width="30%" align="center"><img src="assets/qa_type_statistics_with_rules.png" alt="QA and rule statistics" width="100%"></td>
</tr>
</table>
## Dataset summary
| Resource | Records | Description |
| ----------------------- | --------- | ---------------------------------------------------------------------------------- |
| Rule library | 4,936,113 | Globally shared procedural rules, each in NL and FOL forms. |
| Full QA collection | 3,377,737 | 50,000 Single-Rule, 3,250,000 Parallel Multi-Rule, and 77,737 Multi-Hop instances. |
| Training split | 111,200 | 25,000 Single-Rule, 49,200 Parallel Multi-Rule, and 37,000 Multi-Hop instances. |
| Development split | 3,390 | Development instances. |
| Official evaluation set | 550 | The evaluation split: 550 instances. |
The full QA collection contains eleven fine-grained subtasks: `single-rule`, `multi-rule-2` through `multi-rule-8`, and `multi-hop-2` through `multi-hop-4`. For Multi-Rule QA, the numeric suffix denotes the number of distinct gold rules (with a minimum label of 2); for Multi-Hop QA, it denotes the reasoning depth.
## Repository layout
```text
all_data/
rule_library.txt # Full rule library; recommended rule source
rule_library.pkl # Serialized generation-time representation
single_qa.jsonl # 50,000 Single-Rule QA instances
multi_qa_base.jsonl # 30,000 base Multi-Rule QA instances
multi_qa_generated_{2,3,4}.jsonl # 3,220,000 composed Parallel Multi-Rule instances
multi_hop_qa.jsonl # 77,737 Multi-Hop QA instances
sampled/
train_111200.jsonl # Training split
dev_3390.jsonl # Development split
verified/
test_550.jsonl # Official balanced evaluation set
```
`all_data/rule_library.txt` is tab-separated with the columns `rule_id`, `natural_language_rule`, `fol_rule`, and `rule_type`. Use this text file rather than the pickle file when portability and security matter.
## QA format
Every QA instance is a JSON object. Core fields are:
| Field | Description |
| ------------ | ------------------------------------------------------------------------------------- |
| `id` | Unique QA identifier. |
| `Q` | Natural-language question. |
| `A` | Gold answer list, ordered to match the question or its sub-questions. |
| `Q_template` | Prompt template containing the `{rules}` and `{question}` placeholders. |
| `A_template` | Reference answer template. After formatting, the answer is enclosed in `\boxed{...}`. |
| `proofs` | Gold derivation steps. Integer entries are rule IDs from the rule library. |
| `rule_info` | Structured gold rule metadata used for retrieval supervision. |
| `task_type` | One of `single_rule`, `multi_rule`, or `multi_hop`. |
| `qa_type` | Underlying rule subtype(s) involved in the instance. |
`multi_rule` records additionally contain `rule_width`, and `multi_hop` records contain `rule_depth`. Composed Multi-Rule records include `source_ids`, which preserve the order of their component QA instances. The official evaluation set also stores `final_qa_type`, `selection_seed`, and `subtask`.
## Citation
If you find this dataset helpful in your research, we would kindly appreciate a citation:
```bibtex
@misc{yu2026factualknowledgebenchmarkinglearning,
title={Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models},
author={Bohan Yu and Pengfei Cao and Chen Han and Chenxi Zhou and Zhiheng Zhang and Zhiyang Xie and Wenhao Teng and Xiangwen Liao and Jun Zhao and Kang Liu},
year={2026},
eprint={2608.22753},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2608.22753},
}
```
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