Datasets:
metadata
license: mit
task_categories:
- question-answering
size_categories:
- n<1K
SATQuest Dataset
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
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 clausesm
in the CNF, withm=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:
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:
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:
@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 = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/sdpkjc/SATQuest}},
}