LLM explained: why large language models struggle with mathematics

Author(s): Deepanshu

Originally published on Towards AI.

Why statistical pattern learning fails to yield accurate mathematical calculations

You give GPT-4 a simple question like “What is 2+2?” And it confidently answers “4.” Then you ask it to solve a system of linear equations, and suddenly it starts hallucinating solutions.

The article discusses the challenges faced by large language models (LLMs) in mathematical reasoning, emphasizing their reliance on learning statistical patterns rather than actual mathematical computations. This highlights issues such as the inability of models to understand the mathematics that requires precise answers, and training data limitations that lean toward simple arithmetic while ignoring complex mathematical logic. This text highlights topics such as frequency bias in training data and models’ struggles with high complexity problems, ultimately drawing attention to the need to reevaluate current training methods and architectures to improve the mathematical capabilities of LLMs.

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Published via Towards AI