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. |
Similar Papers
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%. |
How Do Humans Write Code? Large Models Do It the Same Way Too (2024.emnlp-main)
Copied to clipboard
| Challenge: | Program-of-Thought (PoT) replaces natural language-based Chain-ofThough (CoT) but introduces more reasoning errors, such as incorrect formulas or flawed logic, compared to CoT. |
| Approach: | They propose a method that integrates CoT and Program-of-Thought to achieve more accurate reasoning and reinforcement learning. |
| Outcome: | The proposed method achieves an average improvement of 6.5% on the Llama-Base model and 4.3% on the Mistral-Bass model across 8 mathematical calculation datasets. |
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. |
CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution (2025.acl-long)
Copied to clipboard
Ruiyang Xu, Jialun Cao, Yaojie Lu, Ming Wen, Hongyu Lin, Xianpei Han, Ben He, Shing-Chi Cheung, Le Sun
| Challenge: | Existing code benchmarks focus on code generation, while those for code reasoning are insufficient. |
| Approach: | They propose a multi-lingual code reasoning benchmark that contains 19 programming languages and at least 600 subjects for each language. |
| Outcome: | The proposed model trains on Python and achieves 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs. |
Self-Consistency from Only Two Samples: CoT–PoT Ensembling for Efficient LLM Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large language models but it comes at a high computational cost due to extensive sampling. |
| Approach: | They propose a hybrid ensembling approach that leverages the complementary strengths of Chain-of-Thought and Program-of -Thus . they propose encapsulating two different modes of reasoning to create a single output and a final answer is selected as the most frequently occurring one among these outputs. |
| Outcome: | The proposed approach reduces the number of samples required for SC by 9.3x . the majority of tasks can be addressed with only two samples, which has not been possible with prior methods. |
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. |
Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)
Copied to clipboard
Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou
| Challenge: | Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English. |
| Approach: | They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy. |
| Outcome: | The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath). |
A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for eliciting Large Language Models (LLMs) to solve complex tasks are limited to English due to the imbalance in the distribution of pre-training data. |
| Approach: | They propose a method for aligning Cross-lingual CoT reasoning across languages . they propose eliciting Large Language Models to solve complex tasks step-by-step . |
| Outcome: | The proposed method outperforms existing prompting methods by reducing interactions and achieving state-of-the-art performance. |
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field. |
| Approach: | They evaluate ChatGPT over multiple tasks with diverse languages and large datasets to provide more comprehensive information for multilingual NLP applications. |
| Outcome: | The proposed model can process and generate texts for multiple languages due to its multilingual training data. |
MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)
Copied to clipboard
| Challenge: | Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data . |
| Approach: | They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language. |
| Outcome: | The proposed framework improves multilingual reasoning across languages on three benchmarks. |