MultiLingPoT: Boosting Mathematical Reasoning in LLMs through Multilingual Program Integration (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Program-of-Thought is an important way for LLMs to solve mathematical problems. |
| Approach: | They propose a multilingual programme reasoning method that uses program instead of natural language in reasoning and proposes to integrate multilingual integration into the training and inference. |
| Outcome: | The proposed method improves individual language’s reasoning accuracy by 2.5% and improves performance by 8%. |
Similar Papers
Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments (2025.findings-acl)
Copied to clipboard
Patomporn Payoungkhamdee, Pume Tuchinda, Jinheon Baek, Samuel Cahyawijaya, Can Udomcharoenchaikit, Potsawee Manakul, Peerat Limkonchotiwat, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Multi-step reasoning is essential for large language models, yet multilingual performance remains challenging. |
| Approach: | They propose a framework to evaluate Program-of-Thought (PoT) prompting by separating multilingual reasoning from code execution to examine impact of fine-tuning on question-reasoning alignment and reasoning quality. |
| Outcome: | The proposed framework outperforms CoT fine-tuned models in multilingual settings and shows strong correlation between reasoning quality and answer accuracy. |
Multilingual Reasoning via Self-training (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent studies have introduced eclectic strategies to improve reasoning beyond English, but these methods are related to specific language that is not always optimal for reasoning. |
| Approach: | They propose a modular approach that instructs models to structure reasoning passages in a different problem space and then self-refines their capabilities to deliver step-wise reasoning passage. |
| Outcome: | The proposed approach achieves significant improvements in multilingual reasoning of various models and task, with improved reasoning consistency across languages. |
mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing models show low performance for lesser resourced languages, but they can achieve surprising performance on complex reasoning tasks in natural language processing (NLP). |
| Approach: | They compile the first large-scale multilingual math reasoning dataset, *mCoT-MATH*, covering eleven diverse languages. |
| Outcome: | The proposed model achieves impressive consistency across languages and comparable performance to close- and open-source models even of much larger sizes. |
A Survey of Multilingual Reasoning in Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | This survey provides the first in-depth review of multilingual reasoning in Language Models. |
| Approach: | This survey provides the first in-depth review of multilingual reasoning in LMs. |
| Outcome: | The present study provides the first in-depth review of multilingual reasoning in LMs. |
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)
Copied to clipboard
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. |
| Approach: | They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks. |
| Outcome: | The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT. |
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. |
| Approach: | They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. |
| Outcome: | The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages. |
Eliciting Better Multilingual Structured Reasoning from LLMs through Code (2024.acl-long)
Copied to clipboard
| Challenge: | xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks. |
| Approach: | They propose a multilingual structured reasoning and explanation dataset that covers four tasks across six languages and extends the English STREET benchmark to 5 additional diverse languages. |
| Outcome: | The proposed models show improved multilingual performance on scientific commonsense reasoning subtasks and no regression on non-reasoning tasks. |
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research focuses on developing powerful large language models for mathematical reasoning within monolingual languages. |
| Approach: | They propose to use translation to build powerful multilingual math reasoning models . they propose different training strategies to build xMR LLMs that outperform open-source LLM . |
| Outcome: | The proposed model outperforms open-source LLMs and surpasses ChatGPT in few-shot scenarios. |
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025.coling-main)
Copied to clipboard
Yuchun Fan, Yongyu Mu, YiLin Wang, Lei Huang, Junhao Ruan, Bei Li, Tong Xiao, Shujian Huang, Xiaocheng Feng, Jingbo Zhu
| Challenge: | Large language models (LLMs) have demonstrated significant improvements in reasoning abilities, but these improvements are primarily focused on English, leading to inferior performance in non-English scenarios. |
| Approach: | They propose a multilingual reasoning alignment approach that fine-tunes the layers responsible for multilingual comprehension in one stage. |
| Outcome: | The proposed method fine-tunes 6 of the 9 layers responsible for multilingual comprehension, while reducing training time by 4.1-11.9 compared to the two-stage method. |
From Representation to Choice: Tracing Decision Emergence Across Languages in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models have made them highly multilingual, but how they internally reason remains unexplored. |
| Approach: | They propose to model multilingual reasoning through a decision-making perspective using aligned multiple-choice questions from the mMMLU benchmark. |
| Outcome: | The proposed model shows that languages share similar activation spaces, but subtle divergences emerge as decisions propagate through transformer layers. |