\title{The Logical Reasoning Dilemma of LLMs: A Mapping Deficit in Representation}

Authors: Yiling Wu
Primary area: causal reasoning
Keywords: Large Language Models, Representation, Representation Acquisition, Mapping, Psychosemantic Modeling, Semantic Web, Logical Reasoning, Comparison, Categorization, Statistics

Abstract:
\begin{abstract}
Currently, Large Language Models (LLMs) based on the Generative Pre-trained Transformer architecture have made breakthroughs in multilingual language processing tasks and perform at a similar level to humans in these tasks. However, their performance in dealing with tasks involving reasoning, especially logical reasoning, shows significant shortcomings. In this paper, we argue that logical reasoning competence requires a certain type of representation acquisition capability. Based on this, in order to assess whether LLMs have the potential to overcome the shortcomings of their logical reasoning competence in their subsequent development, this work compares the representation acquisition processes of humans and LLMs. This comparison reveals that although LLMs use similar representations to humans in processing multilingual language tasks, they do not have the same representation acquisition capability as humans. There is a fundamental difference in the process of representation acquisition between humans and LLMs, which can be expressed as a $mapping\ deficit$ in LLMs' representation acquisition process. This $mapping\ deficit$ explains why LLMs succeed in processing multilingual language tasks even though they do not have the same representation acquisition capability as humans, and why LLMs' logical reasoning competence shows significant shortcomings. This work aims to enhance the logical reasoning competence of LLMs in the future, and we believe that if the $mapping\ deficit$ in the representation acquisition of LLMs is solved, their logical reasoning competence will also improve.
\end{abstract}

Decision: reject

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