transformer-based models that address mathematical reasoning either through pretraining, hybrid systems, or fine-tuning on specific mathematical tasks
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Challenges: General-purpose transformers Transformer are trained primarily on large corpora of text, which include mathematical problems but lack systematic and rigorous math-specific training. This results in limited capabilities for handling complex calculations or abstract algebraic problems.
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Grokking in Mathematical Reasoning: This is an area of research where models are trained on small datasets of synthetic math problems to encourage grokking, a phenomenon where the model suddenly achieves near-perfect performance after extended training. Researchers are interested in how transformers might be able to “grok” math concepts after seeing many examples.
math data sets: MATH dataset,Aristo
Pretrained transformers on math specific data.
GPT-f represents a significant advancement in the use of transformer-based models for mathematical reasoning,