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, SVAMP, and AQuA, LLMs have competitive performance with most task-specific methods.
“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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Explanations for CommonsenseQA: New Dataset and Models
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Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks
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Teach Me to Explain: A Review of Datasets for Explainable NLP.Preprint
Wiegreffe, S., & Marasović, A. (2021) · arXiv (Cornell University) · Cited by 57 · see citing works
QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships
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Solving Math Word Problems with Multi-Encoders and Multi-Decoders
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Few-Shot Self-Rationalization with Natural Language Prompts
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Learning by Fixing: Solving Math Word Problems with Weak Supervision
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Evidence Sentence Extraction for Machine Reading Comprehension
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Teach Me to Explain: A Review of Datasets for Explainable Natural Language ProcessingPreprint
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Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
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Math Word Problem Solving with Explicit Numerical Values
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Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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