mlsanityData to 7 Oct 2026

SocialIQA: Commonsense Reasoning about Social Interactions

arXiv 1904.09728 ↗First posted 22 Apr 2019cs.CLcited by 33

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Cited by 25 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations (e.g., Q: "Jordan wanted to tell Tracy a secret, so Jordan leaned towards Tracy. Why did Jordan do this?" A: "Make sure no one else could hear"). Through crowdsourcing, we collect commonsense questions along with correct and incorrect answers about social interactions, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Empirical results show that our benchmark is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap). Notably, we further establish Social IQa as a resource for transfer learning of commonsense knowledge, achieving state-of-the-art performance on multiple commonsense reasoning tasks (Winograd Schemas, COPA).

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2019.

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Citing papers per year: 2024: 14; 2025: 16; 2026: 3.

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  9. 2025KORMo: Korean Open Reasoning Model for Everyonecited by 4
  10. 2026Social-R1: Towards Human-like Social Reasoning in LLMscited by 4
  11. 2024LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoningcited by 3
  12. 2025Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuningcited by 3
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  18. 2026Where Norms and References Collide: Evaluating LLMs on Normative Reasoningcited by 2
  19. 2026Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Spacecited by 2
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