mlsanityData to 7 Oct 2026

Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

arXiv 1705.04146 ↗First posted 11 May 2017cs.AI cs.CL cs.LGcited by 642

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Cited by 277 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 642 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2017.

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Citing papers per year: 2021: 23; 2022: 49; 2023: 126; 2024: 173; 2025: 161; 2026: 110.

642 papers.

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  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
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38 more
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