File size: 2,947 Bytes
01b7d2d 789b154 4d9657a 01b7d2d 4d9657a 789b154 4d9657a 789b154 4d9657a 789b154 |
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 |
---
license: mit
size_categories:
- n<1K
task_categories:
- question-answering
- text-generation
tags:
- logical-reasoning
- llm
---
# SATQuest Dataset
Paper: [SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs](https://huggingface.co/papers/2509.00930)
[](https://opensource.org/licenses/MIT)
[](https://github.com/sdpkjc/SATQuest)
[](https://pypi.org/project/satquest/)
**TL;DR.** Synthetic CNF benchmark for LLM reasoning: **140** matched SAT/UNSAT pairs with `n in [3, 16]` and fixed ratio `m=4n`. The dataset stores only CNF formulas and solver stats; use the [`SATQuest`](https://github.com/sdpkjc/SATQuest) Python library to render prompts/answers for SATDP, SATSP, MaxSAT, MCS, and MUS in four formats (math, DIMACS, story, dual story).
## Data fields
- `id`: unique identifier for each row.
- `num_literal`: total number of literals in the unsatisfiable formula.
- `sat_dimacs`: DIMACS representation of the satisfiable CNF.
- `unsat_dimacs`: DIMACS representation of the unsatisfiable CNF.
- `num_variable`: number of variables n, between 3 and 16.
- `num_clause`: number of clauses `m` in the CNF, with `m=4n`.
- `solver_metadatas`: dictionary of PySAT solver statistics (conflicts, decisions, propagations, restarts) for SATSP, MaxSAT, MCS, MUS, SATDP_SAT and SATDP_UNSAT tasks.
## Quick usage
Install the package and load a CNF instance:
```python
from datasets import load_dataset
from satquest import CNF, create_problem, create_question
item = load_dataset('sdpkjc/SATQuest', split='test')[0]
cnf = CNF(dimacs=item['sat_dimacs'])
problem = create_problem('SATSP', cnf) # or 'SATDP', 'MaxSAT', 'MCS', 'MUS'
question = create_question('math') # or 'dimacs', 'story', 'dualstory'
prompt = problem.accept(question)
answer = problem.solution # reference answer
reward = int(problem.check(answer)) # 1 if answer is correct, 0 otherwise
```
## Reproducing the dataset
To build an identical dataset from scratch, clone the repository and run the provided generation script:
```bash
pip install datasets numpy tyro
git clone https://github.com/sdpkjc/SATQuest.git
cd SATQuest
# produce the evaluation set (140 CNF pairs)
python gen_cnf_dataset.py --hf-entity {YOUR_HF_ENTITY} --seed 9527
```
## Citation
Please cite the SATQuest paper if you use this dataset:
```bibtex
@misc{satquest2025,
author = {Yanxiao Zhao, Yaqian Li, Zihao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, Lei Ren, Xiaolin Qin, Kaiwen Long},
title = {SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs},
year = {2025},
publisher = {Hugging Face},
journal = {Hugging Face Papers},
howpublished = {\url{https://huggingface.co/papers/2509.00930}},
}
``` |