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These existing models are susceptible to dependency on shallow heuristics and spurious correlations to derive the solution expressions. The work of solving the problems is done, not by a neural network, but by the symbolic algebra package sympy Problems of various formats are excluded from consideration. In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (llms) into smaller, more efficient student models.
To measure this ability in machine learning models, we introduce math, a new dataset of 12,500 challenging competition mathematics problems.
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