Adversarial Examples for Evaluating Math Word Problem Solvers (2021.findings-emnlp)

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Challenge: Existing MWP solvers do not understand language and its relation with numbers, and their accuracy is unclear.
Approach: They propose two methods to generate adversarial attacks to evaluate the robustness of existing MWP solvers.
Outcome: The proposed method reduces the accuracy of existing MWP solvers by over 40% on two benchmark datasets.

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Challenge: Large language models (LLMs) have revolutionized the educational landscape due to the great improvements in their natural language generation and problem-solving capabilities.
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