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        <title>MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning</title>
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    <abstract>Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge. Several studies have leveraged text retrieval to augment the models’ commonsense ability. Unlike text, images capture commonsense information inherently but little effort has been paid to effectively utilize them. In this work, we propose a novel Multi-mOdal REtrieval (MORE) augmentation framework, to leverage both text and images to enhance the commonsense ability of language models. Extensive experiments on the Common-Gen task have demonstrated the efficacy of MORE based on the pre-trained models of both single and multiple modalities.</abstract>
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%0 Conference Proceedings
%T MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning
%A Cui, Wanqing
%A Bi, Keping
%A Guo, Jiafeng
%A Cheng, Xueqi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F cui-etal-2024-multi
%X Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge. Several studies have leveraged text retrieval to augment the models’ commonsense ability. Unlike text, images capture commonsense information inherently but little effort has been paid to effectively utilize them. In this work, we propose a novel Multi-mOdal REtrieval (MORE) augmentation framework, to leverage both text and images to enhance the commonsense ability of language models. Extensive experiments on the Common-Gen task have demonstrated the efficacy of MORE based on the pre-trained models of both single and multiple modalities.
%U https://aclanthology.org/2024.findings-acl.69
%P 1178-1192

Markdown (Informal)

[MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning](https://aclanthology.org/2024.findings-acl.69) (Cui et al., Findings 2024)

• MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning (Cui et al., Findings 2024)

ACL

• Wanqing Cui, Keping Bi, Jiafeng Guo, and Xueqi Cheng. 2024. MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning. In Findings of the Association for Computational Linguistics ACL 2024, pages 1178–1192, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.