Overview

Recent exploration shows that LLMs, e.g., ChatGPT, may pass the Turing test in human-like chatting but have limited capability even for simple reasoning tasks (Biever, 2023). It remains unclear whether LLMs reason or not (Mitchell, 2023). Human reasoning has been characterized as a dual-process phenomenon (see (Sun, 2023) for a general overview) or as mechanisms of fast and slow thinking (Kahneman, 2011). These findings suggest two directions for exploring neural reasoning: starting from existing neural networks to enhance the reasoning performance with the target of symbolic-level reasoning, and starting from symbolic reasoning to explore its novel neural implementation (Dong et al., 2024). These two directions will ideally meet somewhere in the middle and will lead to representations that can act as a bridge for novel neural computing, which qualitatively differs from traditional neural networks, and for novel symbolic computing, which inherits the good features of neural computing. Hence the name of our workshop, with a focus on Natural Language Processing and Knowledge Graph reasoning. This workshop promotes research in both directions, particularly seeking novel proposals from the second direction.

Invited Speakers

Heng Ji

Heng Ji

University of Illinois Urbana-Champaign

Yansong Fengg

Yansong Feng

Peking University

Erhard Hinrichs

Erhard Hinrichs

University of Tübingen

Yixin Cao

Yixin Cao

Singapore Management University

Tiansi Dong

Tiansi Dong

Fraunhofer IAIS & University of Cambridge

Organizers

Kang Liu

Kang Liu

Chinese Academy of Sciences

Yangqiu Song

Yangqiu Song

The Hong Kong University of Science and Technology

Zhen Han

Zhen Han

Amazon Inc.

Rafet Sifa

Rafet Sifa

University of Bonn

Shizhu He

Shizhu He

Institute of Automation, Chinese Academy of Sciences

Yunfei Long

Yunfei Long

University of Essex