Open access
Date
2023-07Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Solving math story problems is a complex task for students and NLP models alike, requiring them to understand the world as described in the story and reason over it to compute an answer. Recent years have seen impressive performance on automatically solving these problems with large pre-trained language models and innovative techniques to prompt them. However, it remains unclear if these models possess accurate representations of mathematical concepts. This leads to lack of interpretability and trustworthiness which impedes their usefulness in various applications. In this paper, we consolidate previous work on categorizing and representing math story problems and develop MathWorld, which is a graph-based semantic formalism specific for the domain of math story problems. With MathWorld, we can assign world models to math story problems which represent the situations and actions introduced in the text and their mathematical relationships. We combine math story problems from several existing datasets and annotate a corpus of 1,019 problems and 3,204 logical forms with MathWorld. Using this data, we demonstrate the following use cases of MathWorld: (1) prompting language models with synthetically generated question-answer pairs to probe their reasoning and world modeling abilities, and (2) generating new problems by using the world models as a design space. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000653696Publication status
publishedExternal links
Book title
Findings of the Association for Computational Linguistics: ACL 2023Pages / Article No.
Publisher
Association for Computational LinguisticsEvent
Organisational unit
09684 - Sachan, Mrinmaya / Sachan, Mrinmaya
09664 - Schölkopf, Bernhard / Schölkopf, Bernhard
Related publications and datasets
Is supplemented by: https://github.com/eth-nlped/mathworld
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ETH Bibliography
yes
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