GSM-Symbolic: Analyzing LLM Limitations in Mathematical Reasoning and Potential Solutions

The paper "GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models" by Mirzadeh et al. highlights important questions about LLMs' mathematical reasoning capabilities. It introduces GSM-Symbolic, an enhanced benchmark derived from the popular GSM8K dataset, and finds significant variability in model performance across different instantiations of the same question. The study also demonstrates that models are more sensitive to changes in numerical values than to changes in proper names within problems. However, its conclusions may not fully capture the complexity of the issue. Synthetic data generation techniques can address these challenges and push the boundaries of what AI models can achieve in mathematical reasoning tasks.

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