Research map / Vision-language and multimodal / Visual question answering
Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add video features (DenseNet161) to question-answer inputs | Q+A (BERT) · Social-IQ | A2 accuracy: 57.02 -> 63.91 | Helped |
| Add transcript features to question-answer inputs | Q+A (BERT) · Social-IQ | A2 accuracy: 57.02 -> 57.87 | Helped |
| Add acoustic features to question-answer inputs | Q+A (BERT) · Social-IQ | A2 accuracy: 57.02 -> 57.22 | No clear effect |
| Use multimodal Tensor-MFN rather than the strongest reported single bias baseline | Social-IQ QA models · Social-IQ | A2 accuracy: 63.91 -> 64.82 | Helped |
Closest papers by meaning
- VEGAS: Towards Visually Explainable and Grounded Artificial Social Intelligence (AAAI 2025 · similarity 0.89)
- MimeQA: Towards Socially-Intelligent Nonverbal Foundation Models (NeurIPS 2025 · similarity 0.87)
- IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering (NeurIPS 2024 · similarity 0.86)
- SNS-Bench: Defining, Building, and Assessing Capabilities of Large Language Models in Social Networking Services (ICML 2025 · similarity 0.85)
- Mitigating Social Bias in Large Language Models: A Multi-Objective Approach Within a Multi-Agent Framework (AAAI 2025 · similarity 0.85)
- MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems (NeurIPS 2025 · similarity 0.84)
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