Large Language Models (LLMs) may not always exhibit true reasoning abilities, with a potential reliance on memorization instead of learning general techniques.
Synthetic data generation systems like MATH() can be used to explore the reasoning capabilities of LLMs, but may introduce biases if not carefully analyzed and corrected for errors.
Fine-tuning LLMs on specific problem areas can reveal insights into their reasoning abilities, but challenges with longer solutions and complex problem sets may impact performance.
Some math problems in the MATH() dataset have incorrect answers marked during evaluation, possibly due to bugs in question generation or solution calculation code.
Certain math problems in the MATH() dataset are overly complex, requiring lengthy computations or involving very large numbers, making them challenging for un-augmented language models.
The MATH() dataset includes math problems with arithmetic or factorization involving extremely large numbers, which may not accurately test a language model's mathematical reasoning ability.