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BLEU: a method for automatic evaluation of machine translation

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Sequence to Sequence Learning with Neural Networks

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Neural Machine Translation by Jointly Learning to Align and Translate

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Pointer Sentinel Mixture ModelsPreprint

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Pointer networks

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Semantic Parsing on Freebase from Question-Answer Pairs

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Hybrid computing using a neural network with dynamic external memory

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Recurrent Continuous Translation Models

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Rationalizing Neural Predictions

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Solving General Arithmetic Word Problems

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Large language models as tool makers

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HGEN: Learning Hierarchical Heterogeneous Graph Encoding for Math Word Problem Solving

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Self-Evaluation Guided Beam Search for Reasoning

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Mitigating reasoning hallucination through Multi-agent Collaborative Filtering

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Code Execution with Pre-trained Language Models

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Using Intermediate Representations to Solve Math Word Problems

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SemEval-2019 Task 10: Math Question Answering

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Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication

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RA-DIT: Retrieval-Augmented Dual Instruction Tuning

Lin, X. V., Chen, X., Chen, M., Shi, W., Lomeli, M., James, R., Rodriguez, P., Kahn, J., Szilvasy, G., Lewis, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.01352 · Cited by 26

Survey on Evaluation of LLM-based Agents

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Text2Math: End-to-end Parsing Text into Math Expressions

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Don't Make Your LLM an Evaluation Benchmark Cheater

Zhou, K., Zhu, Y., Chen, Z., Chen, W., Zhao, W. X., Chen, X., Lin, Y., Wen, J.-R., & Han, J. (2023) · arXiv · doi:10.48550/arxiv.2311.01964 · Cited by 25

Can Neural Networks Do Arithmetic? A Survey on the Elementary Numerical Skills of State-of-the-Art Deep Learning Models

Testolin, A. (2024) · Applied Sciences · doi:10.3390/app14020744 · Cited by 24

Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange

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MoT: Memory-of-Thought Enables ChatGPT to Self-Improve

Li, X., & Qiu, X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.392 · Cited by 53

A Meaning-Based Statistical English Math Word Problem Solver

Liang, C.-C., Wong, Y.-S., Lin, Y., & Su, K. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1060 · Cited by 23

Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks

Sachan, M., Dubey, A., Hovy, E. H., Mitchell, T. M., Roth, D., & Xing, E. P. (2019) · Computational Linguistics · doi:10.1162/coli_a_00360 · Cited by 25

Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning

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Tab-CoT: Zero-shot Tabular Chain of Thought

Jin, Z., & Lü, W. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.651 · Cited by 28

On the Diversity and Limits of Human Explanations

Tan, C. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.158 · Cited by 33

Chain of Thought Prompting Elicits Knowledge Augmentation

Wu, D., Zhang, J., & Huang, X. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.408 · Cited by 33

Solving arithmetic word problems by scoring equations with recursive neural networks

Zaporojets, K., Bekoulis, G., Deleu, J., Demeester, T., & Develder, C. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114704 · Cited by 24

RODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems

Liu, Q., Guan, W., Li, S., Cheng, F., Kawahara, D., & Kurohashi, S. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3126932 · Cited by 21

SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models

Qin, C., Chen, X., Wang, C., Wu, P., Chen, X., Cheng, Y., Zhao, J., Xiao, M., Dong, X., Long, Q., et al. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737403 · Cited by 31

Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.06904 · Cited by 20

Adaptive-solver framework for dynamic strategy selection in large language model reasoning

Zhou, J., Zhong, W., Wang, Y., & Wang, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2024.104052 · Cited by 19

Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach

Vu, T.-T., & Haffari, G. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1341 · Cited by 19

Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned

Horawalavithana, S., Ayton, E., Sharma, S. K., Howland, S., Subramanian, M., Vasquez, S., Cosbey, R., Glenski, M., & Volkova, S. (2022) · BIGSCIENCE · doi:10.18653/v1/2022.bigscience-1.12 · Cited by 20

Finding Old Answers to New Math Questions: The ARQMath Lab at CLEF 2020

Mansouri, B., Agarwal, A. K., Oard, D. W., & Zanibbi, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45442-5_73 · Cited by 22

Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching

Ding, Y., Hu, H., Zhou, J., Chen, Q., Jiang, B., & He, L. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679881 · Cited by 29

Dual Process Theory for Large Language Models: An overview of using Psychology to address hallucination and reliability issues

Bellini-Leite, S. C. (2023) · Adaptive Behavior · doi:10.1177/10597123231206604 · Cited by 25

Integration of cognitive tasks into artificial general intelligence test for large models

Qu, Y., Wei, C., Du, P., Che, W., Zhang, C., Ouyang, W., Bian, Y., Xu, F., Hu, B., Du, K., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109550 · Cited by 20

NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset

Zhang, Q., Wang, L., Yu, S., Wang, S., Wang, Y., Jiang, J., & Lim, E. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.350 · Cited by 21

Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing ApplicationsPreprint

Hadash, G., Kermany, E., Carmeli, B., Lavi, O., Kour, G., & Jacovi, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09028 · Cited by 54

A Survey of Reasoning with Foundation ModelsPreprint

Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Liu, J., Xu, J., Ding, M., Li, H., Geng, M., et al. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/ac4sp · Cited by 143

Interpretable Charge Prediction for Criminal Cases with Dynamic Rationale Attention

Chao, W., Jiang, X., Luo, Z., Hu, Y., & Ma, W. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11377 · Cited by 17

Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem

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What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study

Madaan, A., Hermann, K. L., & Yazdanbakhsh, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.101 · Cited by 26

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement

Xi, Z., Jin, S., Zhou, Y., Zheng, R., Gao, S., Liu, J., Gui, T., Zhang, Q., & Huang, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.762 · Cited by 40

Rationale-Inspired Natural Language Explanations with Commonsense.Preprint

Majumder, B. P., Camburu, O.-M., Lukasiewicz, T., & McAuley, J. (2021) · arXiv (Cornell University) · Cited by 16

Specialist or Generalist? Instruction Tuning for Specific NLP Tasks

Shi, C., Su, Y., Yang, C. H., Yang, Y., & Cai, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.947 · Cited by 24

NeuReduce: Reducing Mixed Boolean-Arithmetic Expressions by Recurrent Neural Network

Feng, W., Liu, B., Xu, D., Zheng, Q., & Xu, Y. X. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.56 · Cited by 15

MCC-KD: Multi-CoT Consistent Knowledge Distillation

Chen, H., Wu, S., Quan, X., Wang, R., Yan, M., & Zhang, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.454 · Cited by 36

IsarStep: a Benchmark for High-level Mathematical ReasoningPreprint

Li, W., Yu, L., Wu, Y., & Paulson, L. C. (2020) · arXiv (Cornell University) · doi:10.17863/cam.66615 · Cited by 37

NaturalProofs: Mathematical Theorem Proving in Natural LanguagePreprint

Welleck, S., Liu, J., Bras, R. J. L., Hajishirzi, H., Choi, Y., & Cho, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01112 · Cited by 60

Analytical Reasoning of Text

Zhong, W., Wang, S., Tang, D., Xu, Z., Guo, D., Chen, Y., Wang, J., Yin, J., Zhou, M., & Duan, N. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.177 · Cited by 18

Improving intermediate reasoning in zero-shot chain-of-thought for large language models with filter supervisor-self correction

Sun, J., Pan, Y., & Yan, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129219 · Cited by 17

ChatLLM network: More brains, more intelligence

Hao, R., Hu, L., Qi, W., Wu, Q., Zhang, Y., & Nie, L. (2025) · AI Open · doi:10.1016/j.aiopen.2025.01.001 · Cited by 53

Learning to Learn Semantic Parsers from Natural Language Supervision

Labutov, I., Yang, B., & Mitchell, T. M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1195 · Cited by 14

s1: Simple test-time scaling

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., & Hashimoto, T. (2025) · arXiv · doi:10.48550/arxiv.2501.19393 · Cited by 12

How Many Demonstrations Do You Need for In-context Learning?

Chen, J., Chen, L., Zhu, C., & Zhou, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.745 · Cited by 12

Mathematical Information Retrieval: Search and Question Answering

Zanibbi, R., Mansouri, B., & Agarwal, A. K. (2025) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000095 · Cited by 15

Automatic Math Word Problem Generation With Topic-Expression Co-Attention Mechanism and Reinforcement Learning

Wu, Q., Zhang, Q., & Huang, X. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3155284 · Cited by 14

Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations

Camburu, O.-M., Shillingford, B., Minervini, P., Lukasiewicz, T., & Blunsom, P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.382 · Cited by 36

How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI

Kalyan, A., Kumar, A., Chandrasekaran, A., Sabharwal, A., & Clark, P. E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.582 · Cited by 25

Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite TasksPreprint

Mishra, S. R., Mitra, A., Varshney, N., Sachdeva, B., & Baral, C. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.08516 · Cited by 12

Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?

Qin, C., Joty, S., Li, Q., & Zhao, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.659 · Cited by 14

Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts

Liu, T., Guo, Q., Yang, Y., Hu, X., Zhang, Y., Qiu, X., & Zhang, Z. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.169 · Cited by 23

Explainable Natural Language Processing

Søgaard, A. (2021) · Synthesis lectures on human language technologies · doi:10.2200/s01118ed1v01y202107hlt051 · Cited by 32

Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models

Jin, F., Liu, Y., & Tan, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_5 · Cited by 21

A Novel Multi-agent-based Chatbot Approach to Orchestrate Conversational Assistants

Zolitschka, J. F. (2020) · Lecture notes in business information processing · doi:10.1007/978-3-030-53337-3_8 · Cited by 10

GraphMR: Graph Neural Network for Mathematical Reasoning

Feng, W., Liu, B., Xu, D., Zheng, Q., & Xu, Y. X. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.273 · Cited by 11

Semantically-Aligned Equation Generation for Solving and Reasoning Math Word ProblemsPreprint

Chiang, T.-R., & Chen, Y.-N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00720 · Cited by 10

ExPUNations: Augmenting Puns with Keywords and Explanations

Sun, J., Narayan-Chen, A., Oraby, S., Cervone, A., Chung, T., Huang, J., Liu, Y., & Peng, N. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.304 · Cited by 10

Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text

Cao, Q., Kojima, T., Matsuo, Y., & Iwasawa, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.550 · Cited by 15

Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word ProblemsPreprint

Faldu, K., Sheth, A., Kikani, P., Gaur, M., & Avasthi, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05364 · Cited by 9

Two Heads are Better than One: Zero-shot Cognitive Reasoning via Multi-LLM Knowledge Fusion

Liu, L., Zhang, D., Li, S., Zhou, G., & Cambria, E. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679744 · Cited by 9

Students’ difficulties on solving mathematical problem based on ESD objectives

Nikmah, I. L., Juandi, D., & Prabawanto, S. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1157/3/032116 · Cited by 8

Modelling High-Level Mathematical Reasoning in Mechanised Declarative Proofs

Li, W., Lei, Y., Wu, Y., & Paulson, L. C. (2020) · Cited by 8

Defining a New NLP Playground

Li, S., Han, C., Yu, P., Edwards, C., Li, M., Wang, X., Fung, Y. R., Yu, C., Tetreault, J., Hovy, E. H., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.799 · Cited by 11

Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning

Lyu, Y., Hao, J., Wang, Z., Zhao, K., Gao, S., Ren, P., Chen, Z., Wang, F., & Ren, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.145 · Cited by 14

EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers

Kim, B., Ki, K. S., Rhim, S., & Gweon, G. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.305 · Cited by 9

FLamE: Few-shot Learning from Natural Language Explanations

Zhou, Y., Zhang, Y., & Tan, C. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.372 · Cited by 12

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup

Yordanov, Y., Kocijan, V., Lukasiewicz, T., & Camburu, O.-M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.255 · Cited by 12

Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts

Ye, Q., Zha, J., & Ren, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.189 · Cited by 19

When Do Program-of-Thought Works for Reasoning?

Bi, Z., Zhang, N., Jiang, Y., Deng, S., Zheng, G., & Chen, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29721 · Cited by 31

AR-LSAT: Investigating Analytical Reasoning of TextPreprint

Zhong, W., Wang, S., Tang, D., Xu, Z., Guo, D., Wang, J., Yin, J. P., Zhou, M., & Duan, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06598 · Cited by 56

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in FinancePreprint

Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., & Chua, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.07624 · Cited by 8

PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought

Yao, J., Liu, Y., Dong, Z., Guo, M., Hu, H., Keutzer, K., Du, L., Zhou, D., & Zhang, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00671 · Cited by 12

Explainable document-level event extraction via back-tracing to sentence-level event clues

Lu, S., Zhao, G., Li, S., & Guo, J. H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108715 · Cited by 7

Generate Subgoal Images Before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts

Ni, F., Hao, J., Wu, S., Kou, L., Liu, J., Zheng, Y., Wang, B., & Zhuang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01327 · Cited by 13

Unifying the syntax and semantics for math word problem solving

Tao, X., Zhang, Y., Xie, Z., Zhao, Z., Zhou, G., & Lu, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130042 · Cited by 8

Towards Generating Counterfactual Examples as Automatic Short Answer Feedback

Filighera, A., Tschesche, J., Steuer, T., Tregel, T., & Wernet, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11644-5_17 · Cited by 7

Revolutionizing High School Physics Education: A Novel Dataset

Anand, A., Addala, K., Baghel, K. K. S., Goel, A., Hira, M., Gupta, R., & Shah, R. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49601-1_5 · Cited by 8

Does a language model “understand” high school math? A survey of deep learning based word problem solvers

Sundaram, S. S., Gurajada, S., Padmanabhan, D., Abraham, S. S., & Fisichella, M. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1534 · Cited by 31

Operations Guided Neural Networks for High Fidelity Data-To-Text GenerationPreprint

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Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning

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CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks

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Explainable artificial intelligence for natural language processing: A survey

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Mathematical Language Models: A Survey

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Understanding Large Language Models : Towards Rigorous and Targeted Interpretability Using Probing Classifiers and Self-Rationalisation

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A Simple, Yet Effective Approach to Finding Biases in Code Generation

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OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models

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Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning

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An Expression Tree Decoding Strategy for Mathematical Equation Generation

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A Neural Semantic Parser for Math Problems Incorporating Multi-Sentence Information

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Reverse Operation based Data Augmentation for Solving Math Word ProblemsPreprint

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A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-SharingPreprint

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From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning

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Step-by-Step Unmasking for Parameter-Efficient Fine-Tuning of Large Language Models

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Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems

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Program of Equations Thoughts to Solve Algebra Word ProblemsPreprint

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Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

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EquGener: A Reasoning Network for Word Problem Solving by Generating Arithmetic Equations.

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