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GPT Can Solve Mathematical Problems Without a Calculator

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arxiv 2309.03241 v2 pith:NNEZMSEV submitted 2023-09-06 cs.LG cs.AIcs.CL

classification cs.LGcs.AIcs.CL
keywords operationsarithmeticdatawithoutaccuracyaccuratelycalculatorgpt-4
verification ladder T0 review T1 audit T2 compute T3 formal
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Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools. This paper aims to challenge this misconception. With sufficient training data, a 2 billion-parameter language model can accurately perform multi-digit arithmetic operations with almost 100% accuracy without data leakage, significantly surpassing GPT-4 (whose multi-digit multiplication accuracy is only 4.3%). We also demonstrate that our MathGLM, fine-tuned from GLM-10B on a dataset with additional multi-step arithmetic operations and math problems described in text, achieves similar performance to GPT-4 on a 5,000-samples Chinese math problem test set. Our code and data are public at https://github.com/THUDM/MathGLM.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 10 citations worldwide. Full citation record

  1. Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A small transformer learns addition, multiplication, and division by mastering simple digit subtasks first, and human teaching strategies lift its arithmetic accuracy to ~100%.

  2. Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Tool-augmented LLM annotators improve agreement with ground-truth preferences on long-form factual and coding tasks, with mixed results on math, compared to standard LLM-as-a-judge baselines.

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