Commonsense Knowledge
Commonsense knowledge research aims to equip artificial intelligence systems with the everyday understanding that humans possess implicitly. Current research focuses on integrating commonsense into various AI tasks, such as robotics, dialogue systems, and image understanding, often leveraging large language models, graph convolutional networks, and knowledge graphs to represent and reason with this knowledge. This work is crucial for improving AI's reliability, explainability, and ability to interact naturally with humans, ultimately leading to more robust and user-friendly AI applications across numerous domains. The development of new benchmarks and datasets is also a significant focus, enabling more rigorous evaluation and comparison of different approaches.
Papers
Free Lunch for Efficient Textual Commonsense Integration in Language Models
Wanyun Cui, Xingran Chen
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge
Hua Cai, Xuli Shen, Qing Xu, Weilin Shen, Xiaomei Wang, Weifeng Ge, Xiaoqing Zheng, Xiangyang Xue
Editing Common Sense in Transformers
Anshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri, Wenlong Zhao, Xiang Lorraine Li, Sarah Wiegreffe, Niket Tandon
CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering
Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song, Antoine Bosselut