# SocialIQA: Commonsense Reasoning about Social Interactions

arXiv: https://arxiv.org/abs/1904.09728 · HTML: https://mlsanity.com/paper/1904.09728/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 30 of 37)

- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs](https://mlsanity.com/paper/2503.01743/) (2025)
- [OLMoE: Open Mixture-of-Experts Language Models](https://mlsanity.com/paper/2409.02060/) (2024)
- [RedPajama: an Open Dataset for Training Large Language Models](https://mlsanity.com/paper/2411.12372/) (2024)
- [Large Language Model Unlearning via Embedding-Corrupted Prompts](https://mlsanity.com/paper/2406.07933/) (2024)
- [AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models](https://mlsanity.com/paper/2406.13233/) (2024)
- [MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time](https://mlsanity.com/paper/2405.16265/) (2024)
- [From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2509.25373/) (2025)
- [Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies](https://mlsanity.com/paper/2508.10523/) (2025)
- [Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal](https://mlsanity.com/paper/2404.17808/) (2024)
- [Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective](https://mlsanity.com/paper/2504.10839/) (2025)
- [Rethinking Generative Large Language Model Evaluation for Semantic Comprehension](https://mlsanity.com/paper/2403.07872/) (2024)
- [CausalEval: Towards Better Causal Reasoning in Language Models](https://mlsanity.com/paper/2410.16676/) (2024)
- [Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering](https://mlsanity.com/paper/2403.01390/) (2024)
- [GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism](https://mlsanity.com/paper/2501.07890/) (2025)
- [PrefDisco: Benchmarking Proactive Personalized Reasoning](https://mlsanity.com/paper/2510.00177/) (2025)
- [Fast Quiet-STaR: Thinking Without Thought Tokens](https://mlsanity.com/paper/2505.17746/) (2025)
- [DynamicMind: A Tri-Mode Thinking System for Large Language Models](https://mlsanity.com/paper/2506.05936/) (2025)
- [Social-R1: Towards Human-like Social Reasoning in LLMs](https://mlsanity.com/paper/2603.09249/) (2026)
- [A Comprehensive Evaluation on Event Reasoning of Large Language Models](https://mlsanity.com/paper/2404.17513/) (2024)
- [MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?](https://mlsanity.com/paper/2506.13065/) (2025)
- [MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE](https://mlsanity.com/paper/2509.17238/) (2025)
- [KORMo: Korean Open Reasoning Model for Everyone](https://mlsanity.com/paper/2510.09426/) (2025)
- [LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning](https://mlsanity.com/paper/2410.09541/) (2024)
- [Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning](https://mlsanity.com/paper/2507.10085/) (2025)
- [Where Norms and References Collide: Evaluating LLMs on Normative Reasoning](https://mlsanity.com/paper/2602.02975/) (2026)
- [Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space](https://mlsanity.com/paper/2604.04944/) (2026)
- [EVIT: Event-Oriented Instruction Tuning for Event Reasoning](https://mlsanity.com/paper/2404.11978/) (2024)
- [Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models](https://mlsanity.com/paper/2412.15501/) (2024)
- [Think Socially via Cognitive Reasoning](https://mlsanity.com/paper/2509.22546/) (2025)

## 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).
