mlsanityData to 14 Oct 2026

A Puzzle-Based Dataset for Natural Language Inference

arXiv 2112.05742 ↗First posted 10 Dec 2021cs.AIcited by 4

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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.

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

6 of 24 references.

References · 6

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  2. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  3. 2015A large annotated corpus for learning natural language inferencecited by 1,847
  4. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  5. 2020A framework for step-wise explaining how to solve constraint satisfaction problemscited by 8
  6. 2021Natural language understanding for logical gamescited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2025Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzlescited by 56
  2. 2024Puzzle Solving using Reasoning of Large Language Models: A Surveycited by 45
  3. 2023Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Taskscited by 3
  4. 2024RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentationcited by 3