MathFormer: Testing whether symbolic math is pattern matching or reasoning [D]
📰 Reddit r/MachineLearning
A small seq2seq model achieves 98.6% accuracy on symbolic math tasks, suggesting it learns pattern matching rather than mathematical reasoning
Action Steps
- Explore the MathFormer repo on GitHub to understand the model architecture and training data
- Run the model on sample symbolic math expressions to test its accuracy
- Configure the model to handle more complex mathematical expressions and evaluate its performance
- Test the model's ability to generalize to unseen mathematical operators and variables
- Apply the insights from this study to improve the design of future ML models for mathematical reasoning
Who Needs to Know This
ML researchers and engineers can benefit from this study to understand the limitations of current models in learning mathematical concepts, and developers can use the open-sourced MathFormer model for symbolic math tasks
Key Insight
💡 The model's high accuracy suggests it learns structural token transformations rather than mathematical reasoning
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🤖 MathFormer: A 4M param seq2seq model reaches 98.6% accuracy on symbolic math tasks, but is it really learning math? 📝
Key Takeaways
A small seq2seq model achieves 98.6% accuracy on symbolic math tasks, suggesting it learns pattern matching rather than mathematical reasoning
Full Article
Repo link and results - https://github.com/Abhinand20/MathFormer Task: Given a factorized expression like (7-3*z)*(-5*z-9), predict the expanded form -> 15*z\*2-8\*z-63 Key takeaway: A tiny (4M param) seq2seq model trained with no math knowledge reaches ~98.6% accuracy on symbolic math tasks, suggesting it learns structural token transformations rather than any notion of operators or variables.
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