# A Puzzle-Based Dataset for Natural Language Inference

arXiv: https://arxiv.org/abs/2112.05742 · HTML: https://mlsanity.com/paper/2112.05742/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [A framework for step-wise explaining how to solve constraint satisfaction problems](https://mlsanity.com/paper/2006.06343/) (2020)
- [Natural language understanding for logical games](https://mlsanity.com/paper/2110.00558/) (2021)

## Built on by (most cited first, 4 of 4)

- [Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles](https://mlsanity.com/paper/2505.19914/) (2025)
- [Puzzle Solving using Reasoning of Large Language Models: A Survey](https://mlsanity.com/paper/2402.11291/) (2024)
- [Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Tasks](https://mlsanity.com/paper/2401.00290/) (2023)
- [RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation](https://mlsanity.com/paper/2409.16383/) (2024)

## Abstract (arXiv)

We provide here a dataset for tasks related to natural language understanding and natural language inference. The dataset contains logical puzzles in natural language from three domains: comparing puzzles, knighs and knaves, and zebra puzzles. Each puzzle is associated with the entire set of atomic questions that can be generated based on the relations and individuals occurring in the text. For each question we provide the correct answer: entailment, contradiction or ambiguity. The answer's correctness is verified against theorem provers. Good puzzles have two properties: (i) each piece of information is necessary and (ii) no unnecessary information is provided. These properties make puzzles interesting candidates for machine comprehension tasks.