The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
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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Cites
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
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Pointer Sentinel Mixture ModelsPreprint
Merity, S., Xiong, C., Bradbury, J., & Socher, R. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1609.07843 · Cited by 5,058
Vinyals, O., Fortunato, M., & Jaitly, N. (2015) · neural information processing systems · Cited by 2,043
Semantic Parsing on Freebase from Question-Answer Pairs
Berant, J., Chou, A., Frostig, R., & Liang, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1160 · Cited by 1,593
Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature · doi:10.1038/nature20101 · Cited by 1,412
Self-citationRecurrent Continuous Translation Models
Kalchbrenner, N., & Blunsom, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1176 · Cited by 1,277
Self-citationRationalizing Neural Predictions
Lei, T., Barzilay, R., & Jaakkola, T. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1011 · Cited by 777
Solving General Arithmetic Word Problems
Roy, S., & Roth, D. (2016) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1202 · Cited by 702
Generating Visual Explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Chen, L., Berant, J., Le, Q. V., Forbus, K. D., & Lao, N. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1003 · Cited by 409
Learning to Automatically Solve Algebra Word Problems
Kushman, N., Zettlemoyer, L., Barzilay, R., & Artzi, Y. (2014) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p14-1026 · Cited by 400
Neural Programmer-Interpreters
Reed, S., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06279 · Cited by 347
Learning to Solve Arithmetic Word Problems with Verb Categorization
Hosseini, M. J., Hajishirzi, H., Etzioni, O., & Kushman, N. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1058 · Cited by 324
Latent Predictor Networks for Code Generation
Ling, W., Blunsom, P., Grefenstette, E., Hermann, K. M., Kočiský, T., Wang, F., & Senior, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1057 · Cited by 307
Self-citationSolving General Arithmetic Word Problems
Roy, S., & Roth, D. (2016) · arXiv · doi:10.48550/arxiv.1608.01413 · Cited by 275 · cited 2×
“Extensive efforts have been made in the domain of math problem solving (Hosseini et al., 2014; Kushman et al., 2014; Roy and Roth, 2015), which aim at obtaining the correct answer to a given math problem.”
BackgroundNeural Programmer: Inducing Latent Programs with Gradient DescentPreprint
Neelakantan, A., Le, Q. V., & Sutskever, I. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04834 · Cited by 171
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes
Quirk, C., Mooney, R., & Galley, M. (2015) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p15-1085 · Cited by 169
Semantic Parsing as Machine Translation
Andreas, J., Vlachos, A., & Clark, S. C. (2013) · Cited by 82
Latent Predictor Networks for Code GenerationPreprint
Ling, W., Grefenstette, E., Hermann, K. M., Kočiský, T., Senior, A., Wang, F., & Blunsom, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06744 · Cited by 82
Self-citationSemantic Parsing with Bayesian Tree Transducers
Jones, B., Johnson, M. S., & Goldwater, S. J. (2012) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 52
Equation Parsing : Mapping Sentences to Grounded Equations
Roy, S., Upadhyay, S., & Roth, D. (2016) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1117 · Cited by 50
Rationalization: A Neural Machine Translation Approach to Generating Natural Language ExplanationsPreprint
Ehsan, U., Harrison, B. E., Chan, L., & Riedl, M. O. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.07826 · Cited by 43
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak SupervisionPreprint
Chen, L., Berant, J., Le, Q. V., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00020 · Cited by 41
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
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Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257
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Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00626-4 · Cited by 1,191
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2203.11171 · Cited by 829
“For larger models that already achieve high accuracy on most tasks (e.g., GPT-3 and PaLM-540B), self-consistency still contributes significant additional gains with +12%-18% absolute accuracy on tasks like AQuA and GSM8K, and +7%-11% on SVAMP and ASDiv.”
BackgroundMethodInfluential
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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