MORE：多模态检索增强的生成式常识推理
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning
AI总结：
本文提出多模态检索增强框架MORE，同时利用文本和图像增强语言模型的常识推理能力，并在CommonGen任务上验证了其有效性。
摘要：
由于常识信息的记录频率远低于其存在频率，通过文本生成预训练的语言模型难以学习到足够的常识知识。已有若干研究利用文本检索来增强模型的常识能力。与文本不同，图像天然地捕获常识信息，但有效利用图像的努力却很少。在这项工作中，我们提出了一种新颖的多模态检索（MORE）增强框架，以同时利用文本和图像来增强语言模型的常识能力。在CommonGen任务上的大量实验表明，基于单模态和多模态预训练模型的MORE均具有有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
