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Social intelligence is essential for understanding and reasoning about humanexpressions, intents and interactions. One representative benchmark for itsstudy is Social Intelligence Queries (Social-IQ), a dataset of multiple-choicequestions on videos of complex social interactions. We define a comprehensivemethodology to study the soundness of Social-IQ, as the soundness of suchbenchmark datasets is crucial to the investigation of the underlying researchproblem. Our analysis reveals that Social-IQ contains substantial biases, whichcan be exploited by a moderately strong language model to learn spuriouscorrelations to achieve perfect performance without being given the context oreven the question. We introduce DeSIQ, a new challenging dataset, constructedby applying simple perturbations to Social-IQ. Our empirical analysis showsDeSIQ significantly reduces the biases in the original Social-IQ dataset.Furthermore, we examine and shed light on the effect of model size, modelstyle, learning settings, commonsense knowledge, and multi-modality on the newbenchmark performance. Our new dataset, observations and findings open upimportant research questions for the study of social intelligence.

DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding