Papers with word problems
Standardized Tests as benchmarks for Artificial Intelligence (D18-3)
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| Challenge: | Standardized tests have been proposed as replacements to the Turing test as a driver for progress in AI. |
| Approach: | et al. propose standardized tests as replacements to the Turing test as a driver for progress in AI. |
| Outcome: | a series of standardized tests have been proposed as replacements to the Turing test . the tutorial categorizes open domain and closed domain tests into two categories . open domain tests require the system to have significant domain knowledge and reasoning capabilities. |
Predicting Algorithm Classes for Programming Word Problems (D19-55)
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| Challenge: | Using a text classification problem, we map programming word problems to relevant classes of algorithms. |
| Approach: | They propose to map programming word problems to relevant classes of algorithms by using a text classification problem as a classification task. |
| Outcome: | The proposed algorithm class prediction is 9 percent lower than a human on the task. |
Mapping probability word problems to executable representations (2021.emnlp-main)
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Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans
| Challenge: | a recent paper addresses the problem of solving math word problems automatically . a number of approaches have been proposed for solving word problems . |
| Approach: | They employ a sequence-to-sequence model to generate intermediate representations for word problems . they then use a probabilistic programming system to provide the answer . their best performing model incorporates general-domain contextualised word representations . |
| Outcome: | The proposed model is the best performing on a declarative language and a probabilistic programming system. |
HAWP: a Dataset for Hindi Arithmetic Word Problem Solving (2022.lrec-1)
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| Challenge: | Word problem solving is a challenging and interesting task in NLP. |
| Approach: | They propose to use equations to solve Hindi arithmetic word problems . they propose to also use equation equivalence to evaluate word problem solvers . |
| Outcome: | The proposed dataset is based on 2336 arithmetic word problems in Hindi . it also includes baseline systems and evaluation techniques . |
Towards Language Agnostic Universal Representations (P19-1)
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| Challenge: | Current representations in machine learning are language dependent . however, fluent bilingual speakers rarely face trouble translating a task learned in one language to another . |
| Approach: | They propose a method to decouple the language from the problem by learning language agnostic representations. |
| Outcome: | The proposed model achieves similar accuracies in a single language and in another language. |
Disentangling Text and Math in Word Problems: Evidence for the Bidimensional Structure of Large Language Models’ Reasoning (2025.findings-acl)
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Pedro Calais, Gabriel Franco, Zilu Tang, Themistoklis Nikas, Wagner Meira Jr., Evimaria Terzi, Mark Crovella
| Challenge: | Existing studies show that LLMs struggle with text interpretation and equation solving, despite distinct proficiencies in textual and mathematical components. |
| Approach: | They disentangle textual interpretation and mathematical solving steps in word problems drawn from Brazil's largest college entrance exam and popular grade school-level benchmark GSM8K. |
| Outcome: | The proposed model outperforms LLMs in Brazil's largest college entrance exam and popular grade school-level benchmark. |
MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations (2024.findings-emnlp)
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| Challenge: | Existing models and data fail to be educationally appropriate, causing teachers to write boilerplate questions and use boilerplate question sets. |
| Approach: | They propose that large language models (LLMs) can generate educational word problems by generating word problems using annotations from experts. |
| Outcome: | The proposed model generates more solvable, accurate, and appropriate word problems than public models while avoiding harmful questions. |