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.- Anthology ID:
- 2023.emnlp-main.191
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3169–3180
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.191
- DOI:
- 10.18653/v1/2023.emnlp-main.191
- Cite (ACL):
- Xiao-Yu Guo, Yuan-Fang Li, and Reza Haf. 2023. DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3169–3180, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding (Guo et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2023.emnlp-main.191.pdf