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Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
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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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Ott, S., Hebenstreit, K., Liévin, V., Hother, C. E., Moradi, M., Mayrhauser, M., Praas, R., Winther, O., & Samwald, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02433-3 · Cited by 66
Math Word Problem Solving with Explicit Numerical Values
Wu, Q., Zhang, Q., Wei, Z., & Huang, X. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.455 · Cited by 48
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics
Sermanet, P., Ding, T., Zhao, J. W., Xia, F., Dwibedi, D., Gopalakrishnan, K., Chan, C., Dulac-Arnold, G., Maddineni, S., Joshi, N. J., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610216 · Cited by 108
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation
Nie, F., Wang, J., Yao, J.-G., Pan, R., & Lin, C.-Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1422 · Cited by 54
Batch Prompting: Efficient Inference with Large Language Model APIs
Cheng, Z., Kasai, J., & Yu, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.74 · Cited by 95
Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step
Li, L. H., Hessel, J., Yu, Y., Ren, X., Chang, K.-W., & Choi, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.150 · Cited by 148
Higher education assessment practice in the era of generative AI tools
(2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.1.28 · Cited by 37
Sachan, M., Dubey, K. A., & Xing, E. P. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1081 · Cited by 37
Large Language Models Can Self-Correct with Key Condition Verification
Wu, Z., Zeng, Q., Zhang, Z., Tan, Z., Shen, C., & Jiang, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.714 · Cited by 36
“We evaluate P RO C O on three complex reasoning tasks: arithmetic reasoning (GSM8K (Cobbe et al., 2021), AQuA (Ling et al., 2017), and MATH (Hendrycks et al., 2021)); open-domain question answering (NQ (Kwiatkowski et al., 2019), Triv-iaQA (Joshi et al., 2017), WebQ (Berant et al., 2013), and…”
BackgroundMethodInfluentialImproving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning
Yu, W., Wen, Y., Zheng, F., & Xiao, N. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.272 · Cited by 37
Overview of ARQMath 2020: CLEF Lab on Answer Retrieval for Questions on Math
Zanibbi, R., Oard, D. W., Agarwal, A. K., & Mansouri, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58219-7_15 · Cited by 35
REFINER: Reasoning Feedback on Intermediate RepresentationsPreprint
Paul, D., Ismayilzada, M., Peyrard, M., Borges, B., Bosselut, A., West, R., & Faltings, B. V. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.01904 · Cited by 35
An Empirical Investigation of Contextualized Number Prediction
Berg-Kirkpatrick, T., & Spokoyny, D. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.385 · Cited by 38
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving
Wu, Q., Zhang, Q., & Wei, Z. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.127 · Cited by 34
TheoremQA: A Theorem-driven Question Answering Dataset
Chen, W., Yin, M., Ku, M., Lu, P., Wan, Y., Ma, X., Xu, J., Wang, X., & Xia, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.489 · Cited by 141
Pilehvar, M. T., Kartsaklis, D., Prokhorov, V., & Collier, N. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1169 · Cited by 38
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Hong, Y., Li, Q., Gong, R., Ciao, D., Huang, S., & Zhu, S. C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17538 · Cited by 33
Filighera, A., Parihar, S., Steuer, T., Meuser, T., & Ochs, S. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.587 · Cited by 33
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word ProblemsPreprint
Zhao, W., Shang, M., Liu, Y., Wang, L., & Liu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.11506 · Cited by 92
Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution
Fernando, C., Banarse, D., Michalewski, H., Osindero, S., & Rocktäschel, T. (2023) · arXiv · doi:10.48550/arxiv.2309.16797 · Cited by 31
Putting Question-Answering Systems into Practice
Kratzwald, B., & Feuerriegel, S. (2018) · ACM Transactions on Management Information Systems · doi:10.1145/3309706 · Cited by 31
Investigating Math Word Problems using Pretrained Multilingual Language Models
Tan, M., Wang, L., Jiang, L., & Jiang, J. (2022) · Workshop on Mathematical Natural Language Processing (MathNLP) · doi:10.18653/v1/2022.mathnlp-1.2 · Cited by 33
From LSAT: The Progress and Challenges of Complex Reasoning
Wang, S., Liu, Z., Zhong, W., Zhou, M., Wei, Z., Chen, Z., & Duan, N. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3164218 · Cited by 42
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
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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