From Computation to Adjudication: Evaluating Large Language Model Judges on Mathematical Reasoning and Precision Calculation

Recent developments in language models have sparked interest in their potential applications beyond natural language tasks, including domains that require precise mathematical reasoning. The novel concept of using a language model as a judge for mathematical tasks offers a unique perspective on its ability to handle both computational precision and logical reasoning. The study involved an evaluation of GPT-Neo's performance on a series of mathematical reasoning and calculation tasks, ranging from basic arithmetic to complex multi-step problems. The results indicated that while the model excels in basic operations with high accuracy, its performance significantly decreases with the increasing complexity of tasks, particularly those involving abstract reasoning and symbolic manipulation. Analysis of error patterns revealed limitations in the model's processing mechanisms, highlighting a reliance on learned patterns rather than a deeper understanding of mathematical principles. The findings contribute to the understanding of the capabilities and constraints of language models in mathematical contexts, providing a foundational assessment that informs future advancements in artificial intelligence systems designed for complex reasoning tasks.