Cited by 3Natural Language Processing TechniquesTopic ModelingCategorization, perception, and language

Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments

Open access
Payoungkhamdee, Patomporn, Tuchinda, Pume, Baek, Jinheon, Cahyawijaya, Samuel, Udomcharoenchaikit, Can, Manakul, Potsawee, Limkonchotiwat, Peerat, Chuangsuwanich, Ekapol +1 more·2025·Findings of the Association for Computational Linguistics: ACL 2025

Abstract

Patomporn Payoungkhamdee, Pume Tuchinda, Jinheon Baek, Samuel Cahyawijaya, Can Udomcharoenchaikit, Potsawee Manakul, Peerat Limkonchotiwat, Ekapol Chuangsuwanich, Sarana Nutanong. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/2025.findings-acl.817

Cites

0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Program-based reasoning can generalize across linguistic environments through shared executable structure.

    “Cross-lingual reasoning frameworks [70] demonstrate that program-based reasoning can generalize across linguistic environments through shared executable structure”

    From Code as Agent Harness (2026) · cites this paper as [70]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 22

  1. Nuo Chen, Zinan Zheng, Ning Wu, Ming Gong, Dong- mei Zhang, and Jia Li. 2024. Breaking language barriers in multilingual mathematical reasoning: In- sights and observations. In Findings of the Associa- tion for Computational Linguistics: EMNLP 2024, pages 7001-7016, Miami, Florida, USA. Association for Computational Linguistics. · DOI
  2. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023. Program of thoughts prompting: Disentangling computation from reason- ing for numerical reasoning tasks. Transactions on Machine Learning Research.
  3. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. Training verifiers to solve math word prob- lems. arXiv preprint arXiv:2110.14168. · arXiv
  4. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. Pal: program-aided lan- guage models. In Proceedings of the 40th Interna- tional Conference on Machine Learning, ICML'23. JMLR.org.
  5. Zhibin Gou, Zhihong Shao, Yeyun Gong, yelong shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen. 2024. ToRA: A tool-integrated reasoning agent for mathematical problem solving. In The Twelfth International Conference on Learning Repre- sentations.
  6. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schel- ten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mi- tra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 others. 2024. The llama 3 herd of models. Preprint, arXiv:2407.21783. · arXiv
  7. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V. Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D. Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Chris Wilhelm, Luca Sol- daini, and 4 others. 2025. Tulu 3: Pushing fron- tiers in open language model post-training. Preprint, arXiv:2411.15124. · arXiv
  8. Seyed Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar. 2025. GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models. In The Thirteenth International Conference on Learning Representations.
  9. Maxwell Nye, Anders Andreassen, Guy Gur-Ari, Hen- ryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. 2021. Show your work: Scratchpads for intermediate com- putation with language models. ArXiv.
  10. Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, and 1 others. 2023. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950. · arXiv
  11. Shuaijie She, Wei Zou, Shujian Huang, Wenhao Zhu, Xiang Liu, Xiang Geng, and Jiajun Chen. 2024. MAPO: Advancing multilingual reasoning through multilingual-alignment-as-preference optimization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10015-10027, Bangkok, Thai- land. Association for Computational Linguistics. · DOI
  12. Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, and Jason Wei. 2023. Language models are multi- lingual chain-of-thought reasoners. In The Eleventh International Conference on Learning Representa- tions.
  13. NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Hef- fernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, and 20 others. 2022. No language left behind: Scal- ing human-centered machine translation. Preprint, arXiv:2207.04672. · arXiv
  14. Hugo Touvron, Louis Martin, Kevin Stone, Peter Al- bert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, and 1 others. 2023. Llama 2: Open foun- dation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. · arXiv
  15. Han Wang, Archiki Prasad, Elias Stengel-Eskin, and Mohit Bansal. 2024. Soft self-consistency improves language models agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 287-301, Bangkok, Thailand. Association for Computational Linguistics. · DOI
  16. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations.
  17. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, and Denny Zhou. 2022. Chain of thought prompting elicits reasoning in large language models. CoRR, abs/2201.11903. · arXiv
  18. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik R Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. In Thirty-seventh Conference on Neural Information Processing Systems.
  19. Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wen- hao Huang, Huan Sun, Yu Su, and Wenhu Chen. 2024. MAmmoTH: Building math generalist models through hybrid instruction tuning. In The Twelfth In- ternational Conference on Learning Representations.
  20. Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robin- son, Catherine Wu, William Song, Tiffany Zhao, Pranav Vishnu Raja, Charlotte Zhuang, Dylan Z Slack, Qin Lyu, Sean M. Hendryx, Russell Kaplan, Michele Lunati, and Summer Yue. 2024. A careful examination of large language model performance on grade school arithmetic. In The Thirty-eight Con- ference on Neural Information Processing Systems Datasets and Benchmarks Track.
  21. Wenhao Zhu, Shujian Huang, Fei Yuan, Shuaijie She, Jiajun Chen, and Alexandra Birch. 2024. Question translation training for better multilingual reasoning. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8411-8423, Bangkok, Thailand. Association for Computational Linguistics. · DOI
  22. Terry Yue Zhuo. 2024. ICE-score: Instructing large language models to evaluate code. In Findings of the Association for Computational Linguistics: EACL 2024, pages 2232-2242, St. Julian's, Malta. Associa- tion for Computational Linguistics. Method en de fr es ru zh ja th sw bn Cross-lingual Llama2-7B AUC 0.9659 0.9544 0.9782 0.9728 0.9733 0.9622 0.9680 0.9683 0.7376 0.9068 T-Statsitic 28.66 25.67 32.72 31.29 31.82 28.81 29.04 12.87 3.23 10.56 CodeLlama-7B AUC 0.9752 0.9708 0.9661 0.9736 0.9723 0.9606 0.9528 0.9215 0.8524 0.9096 T-Statsitic 28.85 29.85 29.82 31.68 31.36 28.86 25.30 16.48 4.96 9.96 Multilingual Llama2-7B AUC 0.9847 0.9561 0.9725 0.9771 0.9675 0.9714 0.9865 0.9556 0.9437 0.9480 T-Statsitic 31.21 25.70 28.94 28.51 29.04 27.80 36.85 25.14 22.99 21.79 CodeLlama-7B AUC 0.9627 0.9327 0.9627 0.9756 0.9536 0.9511 0.9476 0.9532 0.9537 0.9476 T-Statsitic 25.13 21.46 25.94 28.20 23.93 23.45 22.96 25.07 24.10 23.96 Table 16: T-Statistic and AUC scores for Llama2-7B and CodeLlama-7B across MGSM

Cited by

Sorted by citations

Authors
Payoungkhamdee, P., Tuchinda, P., Baek, J., Cahyawijaya, S., Udomcharoenchaikit, C., Manakul, P., Limkonchotiwat, P., Chuangsuwanich, E., & Nutanong, S.
Affiliations
  • Vidyasirimedhi Institute of Science and Technology (TH)
  • Korea Advanced Institute of Science and Technology (KR)
  • AI Singapore (SG)
  • Chulalongkorn University (TH)
Venue
Findings of the Association for Computational Linguistics: ACL 2025
Published
2025
Pages
15810-15828
OpenAlex
W4412888027
Cited by
0
References
0
FWCI
0.00
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • Vidyasirimedhi Institute of Science and Technology
  • National Research Foundation
  • National Research Foundation Singapore
  • Miljøstyrelsen
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
Keywords
cross-lingual reasoning, multilingual reasoning, chain-of-thought prompting, large language models, multilingual LLMs

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 ›

Are you an author, or do you see a problem with this page? Request a review