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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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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
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Yu, X., Cheng, W., Yang, C., & Zhang, T. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128789 · Cited by 5
Mapping probability word problems to executable representations
Šuster, S., Fivez, P., Totis, P., Kimmig, A., Davis, J. J., Raedt, L. D., & Daelemans, W. M. P. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.294 · Cited by 5
ALERT: Adapt Language Models to Reasoning Tasks
Yu, P., Wang, T., Golovneva, O., AlKhamissi, B., Verma, S. S., Jin, Z., Ghosh, G., Diab, M., & Çelikyılmaz, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.60 · Cited by 25
Towards robust automated math problem solving: a survey of statistical and deep learning approaches
Saraf, A., Kamat, P. V., Gite, S., Kumar, S., & Kotecha, K. V. (2024) · Evolutionary Intelligence · doi:10.1007/s12065-024-00957-0 · Cited by 6
Automatic Prompt Selection for Large Language Models
Do, V.-T., Nguyen, X.-Q., Hoang, V.-K., Nguyen, D., Sabahi, S., Yang, J., Hotta, H., Nguyen, M.-T., & Le, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8180-8_8 · Cited by 20
BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles
Zhang, Y., & Wan, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21430 · Cited by 7
Pretrained Language Models are Symbolic Mathematics Solvers too!Preprint
Noorbakhsh, K., Sulaiman, M., Sharifi, M., Roy, K., & Jamshidi, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03501 · Cited by 16
Lin, Q., Xu, B., Hu, G., Li, Z., Hao, Z., Zhang, K., & Cai, R. (2025) · Applied Intelligence · doi:10.1007/s10489-025-07044-6 · Cited by 12
Scaffolding learning: From specific to generic with large language models
Yin, D. S., & Yin, X. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310409 · Cited by 4
Mining Numbers in Text: A Survey
Yoshida, M. W., & Kita, K. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.98540 · Cited by 6
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset
Wu, H., Hui, W., Chen, Y., Wu, W., Tu, K., & Zhou, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.427 · Cited by 8
Kadlčík, M., Štefánik, M., Sotolář, O., & Martinek, V. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.742 · Cited by 6
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Zheng, H., & Saparov, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.277 · Cited by 7
Xia, Y., Li, F., Liu, Q., Jin, L., Zhang, Z., Sun, X., & Shao, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.046 · Cited by 5
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
Kim, S., Joo, S. J., Jang, Y., Chae, H., & Yeo, J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-demo.23 · Cited by 5
Osada, M., Ricardez, G. A. G., Suzuki, Y., & Taniguchi, T. (2024) · Advanced Robotics · doi:10.1080/01691864.2024.2393408 · Cited by 5
Using Program Induction to Interpret Transition System DynamicsPreprint
Penkov, S., & Ramamoorthy, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00376 · Cited by 8
Kratzwald, B., & Feuerriegel, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07097 · Cited by 4
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Meng, Y., & Rumshisky, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10924 · Cited by 6
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Huang, J., Geiger, A., D’Oosterlinck, K., Wu, Z., & Potts, C. (2023) · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/2023.blackboxnlp-1.24 · Cited by 3
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Borazjanizadeh, N., & Piantadosi, S. T. (2024) · arXiv · doi:10.48550/arxiv.2407.11373 · Cited by 3
“…tasks [27], and the simple and repetitive pattern of reasoning required to solve the problems of the current reasoning benchmarks [11, 32, 28, 10, 19], we present a new dataset that (i) is certainly outside of current models’ training sets, and (ii) each problem necessitates a unique and…”
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Shum, K., Diao, S., & Zhang, T. (2023) · arXiv · doi:10.48550/arxiv.2302.12822 · Cited by 3
Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning.
Piękos, P., Malinowski, M., & Michalewski, H. (2021) · ReIn UW (University of Warsaw) · doi:10.18653/v1/2021.acl-short.49 · Cited by 10
The Impact of Prompting Techniques on the Security of the LLMs and the Systems to Which They Belong
Ivănușcă, T., & Cosmin-Iulian, I. (2024) · Applied Sciences · doi:10.3390/app14198711 · Cited by 8
Faithful Self-Refinement in Mathematical Reasoning via Progressive Back-Translation
Liao, H., Zhu, Z., Hu, S., He, H., & Jin, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890054 · Cited by 3
Liao, H., Tian, J., Hu, S., Zhu, Z., He, H., & Jin, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887828 · Cited by 6
Domain-Aware Reinforcement Learning for Prompt Optimization
Gao, M., Sun, B., Wang, T., Fan, Z., Zhang, T., & Zheng, Z. M. (2025) · Mathematics · doi:10.3390/math13162552 · Cited by 3
Chain-of-Thought Prompt Optimization via Adversarial Learning
Yang, G., Cai, X., Wang, S., & Liu, J. (2025) · Information · doi:10.3390/info16121092 · Cited by 3
Dynamic Voting for Efficient Reasoning in Large Language Models
Xue, M., Liu, D., Lei, W., Ren, X., Yang, B., Xie, J., Zhang, Y., Peng, D., & Lv, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.203 · Cited by 10
ATHENA: Mathematical Reasoning with Thought Expansion
Kim, J., Kim, H., Hahn, J., & Han, Y.-S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1014 · Cited by 3
It Ain’t Over: A Multi-aspect Diverse Math Word Problem Dataset
Kim, J., Kim, Y., Baek, I., Bak, J., & Lee, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.927 · Cited by 7
INFORM : Information eNtropy based multi-step reasoning FOR large language Models
Zhou, C., You, W., Li, J., Ye, J., Chen, K., & Zhang, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.216 · Cited by 4
Stromer‐Galley, J., McKernan, B., Zaman, S., Maganur, C., & Regmi, S. (2025) · Social Science Computer Review · doi:10.1177/08944393251334977 · Cited by 6
Neural networks learn to detect and emulate sorting algorithms from images of their execution traces
Perțicaș, C. F., & Indurkhya, B. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106350 · Cited by 4
Improving compositional generalization for multi-step quantitative reasoning in question answering
Nourbakhsh, A., Jiao, C., Shah, S., & Rosé, C. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.125 · Cited by 3
Kunz, J., Jirenius, M., Holmström, O., & Kuhlmann, M. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.14 · Cited by 4
Generative Chain-of-Thought for Zero-Shot Cognitive Reasoning
Liu, L., Zhang, D., Zhu, S., & Li, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72344-5_22 · Cited by 3
Wu, H. R., Hong, H., Sun, L., Bai, X., & Pu, M. (2024) · Electronics · doi:10.3390/electronics13234581 · Cited by 4
Chain of Thought Prompting in Vision-Language Model for Vision Reasoning Tasks
Ou, J., Zhou, J. J., Dong, Y., & Chen, F. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0351-0_22 · Cited by 3
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Liang, C.-C., Wong, Y.-S., Lin, Y., & Su, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06064 · Cited by 3
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit EditingPreprint
Dong, Y., Li, Z., Rezagholizadeh, M., & Cheung, J. C. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08104 · Cited by 3
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents
Yuan, T., He, Z., Dong, L., Wang, Y., Zhao, R., Xia, T., Xu, L., Zhou, B., Li, F., Zhang, Z., et al. (2024) · arXiv · doi:10.48550/arxiv.2401.10019 · Cited by 2
“Due to the lack of scenario knowledge or reasoning ability, GPT-4o may fail to associate corresponding knowledge and reason in specific scenarios to identify risks.”
Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancy
Feng, Z., Chen, Z., Ma, J., Po, Y. T., Chersoni, E., & Li, B. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1126 · Cited by 2
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM
Kang, H., Zhang, Q., Kundu, S., Jeong, G., Liu, Z., Krishna, T., & Zhao, T. (2024) · arXiv · doi:10.48550/arxiv.2403.05527 · Cited by 2
“To further demonstrate the e ff ective-ness of error reduction, we study the performance variation of GEAR-L when applying low-rank approximation to varying number of tokens with LLaMA3-8B on GSM8k and AQuA (w. CoT).”
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Watanabe, S., Leow, C. S., Hoshino, J., Utsuro, T., & Nishizaki, H. (2024) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc63619.2025.10849072 · Cited by 3
Select prompting with chain-of-thought paired with large language models
Che, X., Wu, W., Chen, Y., Jiang, L., & Li, Q. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131511 · Cited by 3
A survey on mathematical reasoning and optimization with large language models
Forootani, A., Aliabadi, D. E., & Thrän, D. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200712 · Cited by 29
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Liu, D., Wang, G., & Yang, J. (2022) · IEEE Access · doi:10.1109/access.2022.3220777 · Cited by 2
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Ye, M., Kuribayashi, T., Suzuki, J., Kobayashi, G., & Funayama, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.912 · Cited by 4
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Wu, H., Liu, L., Zhao, H. Y., & Zhang, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.847 · Cited by 5
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Ismayilzada, T. (2024) · Data science, machine intelligence, and law · doi:10.1007/978-3-031-61999-1_4 · Cited by 2
Gupta, P., Jain, N., & Bhat, A. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575336 · Cited by 2
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Gaur, S., Dagar, A., Punia, A., & Kumar, P. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3601-0_12 · Cited by 2
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
Ye, R., Ge, R., Yuchi, F., Chai, J., Wang, Y., & Chen, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82240-7_9 · Cited by 8
Recycling Numeracy Data Augmentation with Symbolic Verification for Math Word Problem Solving
Jen, T.-Y., Huang, H., & Chen, H. (2021) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3486622.3494000 · Cited by 2
FERMAT: An Alternative to Accuracy for Numerical Reasoning
Sivakumar, J., & Moosavi, N. S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.838 · Cited by 2
Gupta, R., Venktesh, V., Mohania, M., & Goyal, V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26422-1_22 · Cited by 2
Explaining Transition Systems through Program InductionPreprint
Penkov, S., & Ramamoorthy, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08320 · Cited by 3
Text2Math: End-to-end Parsing Text into Math ExpressionsPreprint
Zou, Y., & Lu, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06571 · Cited by 5
NumNet: Machine Reading Comprehension with Numerical ReasoningPreprint
Ran, Q., Lin, Y., Li, P., Zhou, J., & Liu, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06701 · Cited by 6
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Spokoyny, D., & Berg-Kirkpatrick, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07961 · Cited by 2
Neural-Symbolic Solver for Math Word Problems with Auxiliary TasksPreprint
Qin, J., Liang, X., Hong, Y., Tang, J., & Lin, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.01431 · Cited by 12
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Yan, Y., Li, L., & Zhang, B.-W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129868 · Cited by 1
BMWP: the first Bengali math word problems dataset for operation prediction and solving
Mondal, S., Khatua, D., Mandal, S., Prasad, D. K., & Sekh, A. A. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00243-7 · Cited by 7
Du, J., Zheng, Q., & Li, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-99264-3_17 · Cited by 1
Benchmarking In-Context Learning Strategies of Large Language Models for Math Reasoning Tasks
Rong, Y., Seßler, K., Gözlüklü, E., & Kasneci, E. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3630117 · Cited by 3
Large Language Models Are Multitask Chain-of-Thought Prompting Optimizers
Jin, F., & Tan, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3644134 · Cited by 2
EMRDCM : An experience-enhanced multi-role debate method based on compromise mechanisms
Zhang, Y., Feng, J., Yiming, X., Wang, Z., Yang, T., & Wang, X. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01022-1 · Cited by 1
Liu, Z., Huang, P., Xu, Z., Li, X., Liu, S., Peng, C., Xin, H., Yan, Y., Wang, S., Han, X., et al. (2026) · AI Open · doi:10.1016/j.aiopen.2026.02.002 · Cited by 1
Li, S., Liu, Y., Zheng, Y., Li, M., Nguyen, Q. V. H., & Pan, S. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792537 · Cited by 1
Solving Simple Arithmetic Word Problems Precisely with Schemas
Sundaram, S. S., & Abraham, S. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92058-0_52 · Cited by 1
A Math Word Problem Solver Based on Deep Adaptive Graph Convolutional Networks
Jiang, G., Li, S., & Zhang, L. (2022) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic57696.2022.10075288 · Cited by 1
Spokoyny, D., Wu, C.-S., & Xiong, C. (2022) · Workshop on Mathematical Natural Language Processing (MathNLP) · doi:10.18653/v1/2022.mathnlp-1.5 · Cited by 1
Solving Arithmetic English Word Problems Using Intermediate Representation
Kotwal, H., & Patnaik, G. K. (2023) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea58933.2023.10391982 · Cited by 1
Specialized Mathematical Solving by a Step-By-Step Expression Chain Generation
Zhang, W., Shen, Y., Hou, G., Wang, K., & Lü, W. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3410028 · Cited by 1
Large Model Fine-Tuning Method Based on Pre-Cognitive Inductive Reasoning - PCIR
Zhang, H., Wang, L., Shao, H., Wu, M., & Ren, W. (2024) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit61980.2024.10581497 · Cited by 1
Rationality of Thought Improves Reasoning in Large Language Models
Gou, T., Zhang, B., Sun, Z., Wang, J., Liu, F., Wang, Y., & Wang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5501-1_26 · Cited by 1
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Cao, H., & Chen, Y. (2024) · International Conference on Electronic Communication and Artificial Intelligence (ICECAI) · doi:10.1109/icecai62591.2024.10674892 · Cited by 1
Automatic Prompt Generation Based on Combinatorial Relationships in Reasoning Problems
Wu, T., & He, H. (2024) · doi:10.1109/icnlp60986.2024.10692467 · Cited by 1
Advances in reasoning by prompting large language models: A survey
Hong, R., Pang, X., & Zhang, C. (2026) · Cybernetics and Intelligence · doi:10.26599/cai.2024.9390004 · Cited by 3
EquGener: A Reasoning Network for Word Problem Solving by Generating Arithmetic Equations.
Mishra, P., Kurisinkel, L. J., Sharma, D. M., & Varma, V. (2018) · Institutional Repositories DataBase (IRDB) · Cited by 1
Galitsky, B. A. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-52167-7_10 · Cited by 1
Hint of Pseudo Code (HoPC): Zero-Shot Step by Step Pseudo Code Reasoning Prompting
Lei, I. T., Zhu, Z., Yu, H., Yao, Y., & Deng, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4088-4_36 · Cited by 1
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Wang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.12878 · Cited by 3
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Most, A. K., Hu, M., Yang, H., Liu, T., Chen, X., Li, S., Xu, S. X., Liu, Z., & Sikora, A. N. (2024) · medRxiv · doi:10.1101/2024.03.21.24304667 · Cited by 1
TopoRefine: Iterative Refinement with Reasoning Topology as High-Level Feedback
Liao, H., Hu, S., Zhu, Z., He, H., & Jin, Y. (2025) · ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49660.2025.10888346
SparseX: Efficient Segment-Level KV Cache Sharing for Interleaved LLM Serving
Zhang, Q., Chen, K., Liao, N., Lin, Z., Tang, B., Xiong, F., Li, Z., & Wang, X. (2026) · arXiv · doi:10.48550/arxiv.2606.01751
“…tasks cover a broad range of domains to verify applicability in collaborative reasoning systems, including MATH [25], GSM-Hard [26], and AQUA-RAT [27] for mathematical reasoning; SciBench [28] and GPQA [29] for scientific reasoning; MMLU-Pro [30] for general knowledge understanding; and MedQA…”
Shen, X., Liu, Y., Dai, Y., Wang, Y., Miao, R., Tan, Y., Pan, S., & Wang, X. (2025) · arXiv · doi:10.48550/arxiv.2505.23352 · Cited by 6
“…(Hendrycks et al., 2021), mathematical reasoning: GSM8K (Cobbe et al., 2021), Multi-Arith (Roy and Roth, 2015), SVAMP (Patel et al., 2021), and AQuA (Ling et al., 2017), and code generation: HumanEval (Chen et al., 2021), following the evaluation setup adopted in G-designer (Zhang et al., 2025b).”
Epilogue: Frontiers of NLP in the Deep Learning Era
Deng, L., & Liu, Y. (2018) · Springer eBooks · doi:10.1007/978-981-10-5209-5_11 · Cited by 2
Learning to Learn Semantic Parsers from Natural Language SupervisionPreprint
Labutov, I., Yang, B., & Mitchell, T. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08373 · Cited by 3
Reasoning with Sets to Solve Simple Word Problems Automatically
Sundaram, S. S., & Khemani, D. (2017) · International conference natural language processing
Neural Sequence Modeling in Physical Language Understanding
Bleiweiss, A. (2019) · International Joint Conference on Computational Intelligence · doi:10.5220/0008071104640472
DEVELOPMENT OF LARGE SCALE STEM PROBLEM DATABASES FOR STUDENT LEARNING AND ASSESSMENT TOOLS
Davis, J. A., & Lorimer, S. (2021) · Proceedings of the Canadian Engineering Education Association (CEEA) · doi:10.24908/pceea.vi0.14830
Generating Equations from Math Word Problem Using Deep Learning Approach
Sarkar, S., Das, D., Pakray, P., & Pinto, D. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-10766-5_19
BMWP: The First Bengali Math Word Problems Dataset for Operation Prediction and SolvingPreprint
Mondal, S., Khatua, D., Mandal, S., & Sekh, A. A. (2023) · Research Square · doi:10.21203/rs.3.rs-3284558/v1
Gupta, R., Venktesh, V., Mohania, M., & Goyal, V. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614940
Mendoza, P. J. C., Caliwag, J. A., Pagaduan, R. A., Cayabyab, G. T., Patilano, H. S. L., & Borja, J. M. F. D. (2023) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icset59111.2023.10295092
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.
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