Making math AI models smarter by learning multiple ways to solve problems, just like humans do
This paper introduces a method to help AI solve math word problems by teaching a smaller model to generate multiple correct solution equations, learning from a larger teacher model while staying computationally efficient.
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https://arxiv.org/abs/2501.03670
🤔 Original Problem:
Current AI models for solving math word problems can only generate one solution equation, even though many equivalent equations could solve the same problem. This limits their real-world usefulness, especially in educational applications.
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🔧 Solution in this Paper:
→ Introduces DivKD (Diversity-enhanced Knowledge Distillation) that enables student models to learn diverse solution patterns from teacher models
→ Uses Adaptive Knowledge Distillation to selectively transfer high-quality knowledge, filtering out incorrect solutions
→ Incorporates a Conditional Variational Autoencoder to help the student model understand different ways to write equivalent equations
→ Maintains computational efficiency by using a single decoder instead of multiple decoders
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💡 Key Insights:
→ Multiple correct equations can solve the same math problem, but datasets only provide one solution
→ Teacher models sometimes generate incorrect solutions that shouldn't be taught to student models
→ Using multiple decoders increases computational cost without necessarily improving diversity
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📊 Results:
→ Achieved 86.7% accuracy on Math23K dataset
→ Improved performance by 2.8% over baseline Graph2Tree-Z model
→ Maintained similar inference time as base models while generating more diverse solutions
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