ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning
Yuxi Xie, Guanzhen Li, Min-Yen Kan
Abstract
We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. ECHo employs real-world human-centric deductive information building on a television crime drama. ECHo requires the Theory-of-Mind (ToM) ability to understand and reason about social interactions based on multimodal information. Using ECHo, we propose a unified Chain-of-Thought (CoT) framework to assess the reasoning capability of current AI systems. Our ToM-enhanced CoT pipeline accommodates various large foundation models in both zero-shot and few-shot visio-linguistic reasoning. We use this framework to scrutinize recent large foundation models such as InstructGPT and MiniGPT-4 on three diagnostic human-centric tasks. Further analysis demonstrates ECHo as a challenging dataset to expose imperfections and inconsistencies in reasoning. Our data and code are publicly available at [https://github.com/YuxiXie/ECHo](https://github.com/YuxiXie/ECHo).
BibTeX
@inproceedings{
xie2023echo,
title={{ECH}o: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning},
author={Yuxi Xie and Guanzhen Li and Min-Yen Kan},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=mDgLGrL6ze}
}