Case Study: Testing Model Capabilities in Some Reasoning Tasks
Min Zhang Sato Takumi Jack Zhang Jun Wang
Large Language Models (LLMs) excel in generating personalized content and
facilitating interactive dialogues, showcasing their remarkable aptitude for a
myriad of applications. However, their capabilities in reasoning and providing
explainable outputs, especially within the context of reasoning abilities,
remain areas for improvement. In this study, we delve into the reasoning
abilities of LLMs, highlighting the current challenges and limitations that
hinder their effectiveness in complex reasoning scenarios.