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
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement
Hana Kim, Kai Tzu-iunn Ong, Seoyeon Kim, Dongha Lee, Jinyoung Yeo
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases
Quyet V. Do, Tianqing Fang, Shizhe Diao, Zhaowei Wang, Yangqiu Song