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        <title>ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning</title>
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    <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).</abstract>
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%0 Conference Proceedings
%T ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning
%A Xie, Yuxi
%A Li, Guanzhen
%A Kan, Min-Yen
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F xie-etal-2023-echo
%X 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).
%R 10.18653/v1/2023.findings-emnlp.268
%U https://aclanthology.org/2023.findings-emnlp.268
%U https://doi.org/10.18653/v1/2023.findings-emnlp.268
%P 4064-4085

Markdown (Informal)

[ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning](https://aclanthology.org/2023.findings-emnlp.268) (Xie et al., Findings 2023)

• ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning (Xie et al., Findings 2023)

ACL

• Yuxi Xie, Guanzhen Li, and Min-Yen Kan. 2023. ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4064–4085, Singapore. Association for Computational Linguistics.