Exploring the Boundaries of understanding: A Comprehensive study on the Capabilities of large Language Models

Abstract

This paper presents a comprehensive study aimed at exploring the boundaries of understanding within LLMs, specifically focusing on their ability to process, generate, and comprehend complex information. By utilizing a series of benchmarks and experimental scenarios, we scrutinize how LLMs handle ambiguous queries, intricate reasoning, and multi-turn interactions. The study also evaluates the impact of different training data configurations and model architectures on performance outcomes. Key findings reveal that while LLMs exhibit remarkable proficiency in generating coherent and contextually relevant text, their understanding remains constrained by inherent limitations. Specifically, LLMs often struggle with tasks requiring deep logical reasoning, complex inference, and nuanced comprehension of context beyond surface-level patterns. This research underscores the need for ongoing refinement of LLMs, emphasizing the development of strategies to enhance their understanding and reasoning capabilities. By mapping the current boundaries of LLM performance, we provide insights into potential pathways for future advancements in AI and natural language processing. Overall, our findings contribute to a deeper understanding of the strengths and limitations of LLMs, offering valuable perspectives for researchers and practitioners aiming to leverage these models for more sophisticated and reliable applications.