Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs

TL;DR

This study evaluates large language models' reasoning skills across various domains, revealing strengths in analogical and moral reasoning but limitations in spatial reasoning, to inform future AI development.

Contribution

It provides a comprehensive analysis of LLMs' domain-specific reasoning abilities, highlighting their strengths and weaknesses across multiple reasoning tasks.

Findings

LLMs excel at analogical and moral reasoning

LLMs struggle with spatial reasoning

Experiments inform future LLM development

Abstract

The potential of large language models (LLMs) to reason like humans has been a highly contested topic in Machine Learning communities. However, the reasoning abilities of humans are multifaceted and can be seen in various forms, including analogical, spatial and moral reasoning, among others. This fact raises the question whether LLMs can perform equally well across all these different domains. This research work aims to investigate the performance of LLMs on different reasoning tasks by conducting experiments that directly use or draw inspirations from existing datasets on analogical and spatial reasoning. Additionally, to evaluate the ability of LLMs to reason like human, their performance is evaluted on more open-ended, natural language questions. My findings indicate that LLMs excel at analogical and moral reasoning, yet struggle to perform as proficiently on spatial reasoning tasks. I believe these experiments are crucial for informing the future development of LLMs, particularly in contexts that require diverse reasoning proficiencies. By shedding light on the reasoning abilities of LLMs, this study aims to push forward our understanding of how they can better emulate the cognitive abilities of humans.