mlsanityData to 7 Oct 2026

A Review of Winograd Schema Challenge Datasets and Approaches

arXiv 2004.13831 ↗First posted 23 Apr 2020cs.CL cs.AIcited by 27

Not on the map. Why?

Abstract (arXiv)

The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 4; 2024: 4; 2025: 3; 2026: 2.

27 papers.

Sort:

By area:

  1. 2022Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionalitycited by 523
  2. 2022Meaning without reference in large language modelscited by 76
  3. 2021Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schemacited by 32
  4. 2022WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Modelscited by 20
  5. 2023Sabi'a: Portuguese Large Language Modelscited by 19
  6. 2023BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXamscited by 15
  7. 2024Efficiency optimization of large-scale language models based on deep learning in natural language processing taskscited by 14
  8. 2024When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Modelscited by 12
  9. 2024Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimizationcited by 12
  10. 2023Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Modelscited by 9
  11. 2025C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversationscited by 9
  12. 2024Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Modelscited by 7
15 more
  1. 2021The Case for Claim Difficulty Assessment in Automatic Fact Checkingcited by 5
  2. 2025Saliency-driven Dynamic Token Pruning for Large Language Modelscited by 5
  3. 2025Multilingual Test-Time Scaling via Initial Thought Transfercited by 5
  4. 2021Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Modelscited by 4
  5. 2022Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Modelscited by 3
  6. 2022Analyzing Multi-Task Learning for Abstractive Text Summarizationcited by 3
  7. 2026HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Trainingcited by 3
  8. 2021Unsupervised Pronoun Resolution via Masked Noun-Phrase Predictioncited by 2
  9. 2022On Reality and the Limits of Language Data: Aligning LLMs with Human Normscited by 2
  10. 2026MI-PRUN: Optimize Large Language Model Pruning via Mutual Informationcited by 2
  11. 2021Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledgecited by 1
  12. 2021Attention-based Contrastive Learning for Winograd Schemascited by 1
  13. 2022Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoningcited by 1
  14. 2021K"unstliche Intelligenz, quo vadis?cited by 0
  15. 2023Experience and Prediction: A Metric of Hardness for a Novel Litmus Testcited by 0