Papers › SocialIQA: Commonsense Reasoning about Social Interactions

SocialIQA: Commonsense Reasoning about Social Interactions

22 Apr 2019arXiv:1904.09728archive 2025-07-28

Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, Yejin Choi

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).

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create_prompt_davinci clear-nus/llm-human-model/manners_db/manners_db_experiment_utils.py community (archive-listed) unverified MIT (permissive) · d64904c8e899cc6f · report
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get_plot_color clear-nus/llm-human-model/table_clearing/table_clearing_trust_pomdp/general-model/plots/colors.py community (archive-listed) unverified MIT (permissive) · 74e6e524d6f72496 · report
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Tasks

Common Sense ReasoningCoreference ResolutionMultiple-choiceQuestion AnsweringTransfer Learning

Datasets

Introduced by this paper, per the archive.

SIQA

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Coreference Resolution Winograd Schema Challenge BERT-SocialIQA 340M Accuracy 72.5 #31 of 82 Archive leaderboard report
Coreference Resolution Winograd Schema Challenge BERT-large 340M Accuracy 67 #39 of 82 Archive leaderboard report
Question Answering COPA BERT-SocialIQA 340M Accuracy 83.4 #34 of 60 Archive leaderboard report
Question Answering COPA BERT-large 340M Accuracy 80.8 #36 of 60 Archive leaderboard report
Question Answering SIQA BERT-large 340M (fine-tuned) Accuracy 64.5 #13 of 24 Archive leaderboard report
Question Answering SIQA BERT-base 110M (fine-tuned) Accuracy 63.1 #14 of 24 Archive leaderboard report
Question Answering SIQA GPT-1 117M (fine-tuned) Accuracy 63 #15 of 24 Archive leaderboard report
Question Answering SIQA Random chance baseline Accuracy 33.3 #24 of 24 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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