Author's): Dipanshu
Originally published in Towards Artificial Intelligence.
Why statistical pattern learning fails for accurate mathematical calculations
You ask GPT-4 a simple question, such as “What is 2+2?” and probably answers “4”. Then you ask him to solve a system of linear equations and suddenly he starts coming up with solutions.
This article discusses the challenges facing large language models (LLMs) in mathematical reasoning, highlighting their reliance on statistical pattern learning rather than actual mathematical computations. It highlights issues such as the inability of models to understand mathematics, which requires accurate answers, and the limitations of training data, which leans toward simpler arithmetic while neglecting complex mathematical reasoning. The text delves into topics such as frequency bias in training data and the struggles of models with problems of greater complexity, ultimately drawing attention to the need to re-evaluate current training methods and architectures to improve the mathematical capabilities of LLM.
Read the entire blog for free on Medium.
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