Detecting conflicting requirements early in the software development lifecycle is crucial to mitigating risks of system failures and enhancing overall reliability. While Large Language Models (LLMs) have demonstrated proficiency in natural language understanding tasks, they often struggle with the nuanced reasoning required for identifying complex requirement conflicts. This paper introduces a novel framework, SAT-LLM, which integrates Satisfiability Modulo Theories (SMT) solvers with LLMs to enhance the detection of conflicting software requirements. SMT solvers provide rigorous formal reasoning capabilities, complementing LLMs’ proficiency in natural language processing. By synergizing these strengths, SAT-LLM aims to overcome the limitations of standalone LLMs in handling intricate requirement interactions. The early experiments provide empirical evidence supporting the effectiveness of our SAT-LLM over pure LLM-based methods like ChatGPT in identifying and resolving conflicting requirements. These findings lay a foundation for further exploration and refinement of hybrid approaches that integrate advanced NLP techniques with formal reasoning methodologies to address complex challenges in software development.