Case Study: Testing Model Capabilities in Some Reasoning Tasks

In the rapidly evolving field of artificial intelligence [30], Large Language Models (LLMs) have emerged as a cornerstone of technological advancement, revolutionizing the way we interact with machines and process information. With their unparalleled ability to generate human-like text, LLMs have found applications across a broad spectrum of domains, from automating customer service interactions to aiding in the creative process of writing and design. Their proficiency in generating personalized content and facilitating interactive dialogues has underscored their versatility and adaptability, making them indispensable tools in the modern digital landscape [4, 5, 6]. Despite these significant achievements, LLMs are not without their shortcomings. One of the critical areas where LLMs still face challenges is in their reasoning abilities and the provision of explainable outputs.