Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities. The article examines the potential of neuro-symbolic approaches tailored for grounding tasks, showcasing how they can more comprehensively address grounding. Finally, we discuss areas for further exploration and development in grounding.
Symbol Emergence in Cognitive Developmental Systems: a Survey
Ritsumeikan University（立命馆大学）；Bogazici University（海峡大学）；Czech Technical University in Prague（布拉格捷克理工大学）；Queen Mary University of London（伦敦玛丽女王大学）；The University of Electro-communications（电气通信大学）；Council for Scientific and Industrial Research（科学与工业研究理事会）；University of the Witwatersrand（金山大学）；Chiba University（千叶大学）；Okayama Prefectural University（冈山县立大学）；Ozyegin University（厄兹耶因大学）；Universität Innsbruck（因斯布鲁克大学）
Analyzing Cognitive Differences Among Large Language Models through the Lens of Social Worldview
Wangxuan Institute of Computer Technology, Peking University（北京大学计算机技术研究院）；Information Management Department, Peking University（北京大学信息管理系）；Renmin University of China（中国人民大学）
Cognitive phantoms in LLMs through the lens of latent variables
Department of Methodology and Statistics（方法学与统计学系）；Tilburg University（蒂尔堡大学）；Department of Security and Crime Science（安全与犯罪科学系）；University College London（伦敦大学学院）
From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions
City University of Hong Kong（香港城市大学）；Zhejiang Normal University（浙江师范大学）；Squirrel Ai Learning；University of Science and Technology of China（中国科学技术大学）；Wuhan University（武汉大学）
