A Survey of Commonsense Reasoning in LLMs

Abstract

Commonsense reasoning plays a crucial role in natural language processing by enabling systems to determine causation and assess the plausibility of various scenarios. Despite significant research focused on formalizing commonsense knowledge and integrating it with mathematical logic, replicating such reasoning in artificial intelligence systems—such as large language models (LLMs)—remains a formidable challenge. Despite these challenges, LLMs demonstrate significant promise and exhibit ongoing progress in reasoning capabilities. This paper surveys the current state of commonsense reasoning in LLMs, including datasets, models, benchmarks, enhancements, opportunities, and challenges—highlighting both advancements and limitations. We discuss the role of commonsense reasoning in LLMs, how they strive to emulate human-like reasoning, and why they often struggle to capture the nuanced inferences associated with commonsense understanding of the physical and social world. This survey emphasizes the need for continued investigation and optimization, including the integration of mechanisms to enhance the reliability of these models. Overall, while LLMs are making strides in commonsense reasoning, overcoming their limitations requires further research and development to fully harness their potential.