This research paper investigates the mathematical reasoning abilities of large language models (LLMs) and finds that their performance on mathematical problems is not as robust as initially thought. The authors introduce a new benchmark, GSM-Symbolic, which generates diverse versions of math problems to assess LLMs' reasoning skills more thoroughly. Their findings indicate that LLMs struggle to handle variations in numerical values, exhibit a performance decline with increased question complexity, and are vulnerable to irrelevant information within a problem, suggesting their reasoning capabilities might be based on pattern matching rather than true logical understanding. This highlights the limitations of current LLMs in performing genuine mathematical reasoning and emphasizes the need for further research to develop more robust and reliable models.