Abstract
Social intelligence is essential for understanding and reasoning about human expressions, intents and interactions. One representative benchmark for its study is Social Intelligence Queries (Social-IQ), a dataset of multiple-choice questions on videos of complex social interactions. We define a comprehensive methodology to study the soundness of Social-IQ, as the soundness of such benchmark datasets is crucial to the investigation of the underlying research problem. Our analysis reveals that Social-IQ contains substantial biases, which can be exploited by a moderately strong language model to learn spurious correlations to achieve perfect performance without being given the context or even the question. We introduce DeSIQ, a new challenging dataset, constructed by applying simple perturbations to Social-IQ. Our empirical analysis shows DeSIQ significantly reduces the biases in the original Social-IQ dataset. Furthermore, we examine and shed light on the effect of model size, model style, learning settings, commonsense knowledge, and multi-modality on the new benchmark performance. Our new dataset, observations and findings open up important research questions for the study of social intelligence.
Original language | English |
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Title of host publication | Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing |
Editors | Nadi Tomeh, Atsushi Fujita, Aixin Sun, Bin Wang, Rong Tong |
Place of Publication | Stroudsburg PA USA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 3169-3180 |
Number of pages | 12 |
ISBN (Electronic) | 9798891760608 |
DOIs | |
Publication status | Published - 2023 |
Event | Empirical Methods in Natural Language Processing 2023 - , Singapore Duration: 6 Dec 2023 → 10 Dec 2023 https://2023.emnlp.org/ https://aclanthology.org/volumes/2023.findings-emnlp/ (Proceedings) https://aclanthology.org/volumes/2023.emnlp-demo/ (Proceedings) |
Conference
Conference | Empirical Methods in Natural Language Processing 2023 |
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Abbreviated title | EMNLP 2023 |
Country/Territory | Singapore |
Period | 6/12/23 → 10/12/23 |
Internet address |