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Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
Open accessAbstract
Solving algebraic word problems requires executing a series of arithmetic operations-a program-to obtain a final answer.However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge.To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps.Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones.To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales.Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.
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“On GSM8k [29], SVAMP [92], and AQuA [71], LLMs, as generalists, have competitive performance with most methods that have task-specific designs.”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [71]
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BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
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Vinyals, O., Fortunato, M., & Jaitly, N. (2015) · neural information processing systems · Cited by 2,043
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Kalchbrenner, N., & Blunsom, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1176 · Cited by 1,277
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Metadata
- Authors
- Ling, W., Yogatama, D., Dyer, C., & Blunsom, P.
- Affiliations
- University of Oxford (GB)
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 158-167
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2962800603
- Cited by
- 325
- References
- 24
- Citations by year
- 2026: 31 · 2025: 49 · 2024: 39 · 2023: 64 · 2022: 30 · 2021: 33 · 2020: 27 · 2019: 27 · 2018: 21 · 2017: 4
- FWCI
- 9.85
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Mathematics, Computing, and Information Processing
- Keywords
- program induction, algebra word problems, indirect supervision, mathematical reasoning, natural language explanations
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