@inproceedings{guo-etal-2023-desiq,
title = "{D}e{SIQ}: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding",
author = "Guo, Xiao-Yu and
Li, Yuan-Fang and
Haf, Reza",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-main.191/",
doi = "10.18653/v1/2023.emnlp-main.191",
pages = "3169--3180",
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. 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 De-SIQ 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."
}
Markdown (Informal)
[DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding](https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-main.191/) (Guo et al., EMNLP 2023)
ACL