Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments (2025)

Abstract: Multi-step reasoning is essential for large language models (LLMs), yet multilingual performance remains challenging. While Chain-of-Thought (CoT) prompting improves reasoning, it struggles with non-English languages due to the entanglement of reasoning and execution. Program-of-Thought (PoT) prompting separates reasoning from execution, offering a promising alternative but shifting the challenge to generating programs from non-English questions. We propose a framework to evaluate PoT by separating multilingual reasoning from code execution to examine (i) the impact of fine-tuning on question-reasoning alignment and (ii) how reasoning quality affects answer correctness. Our findings demonstrate that PoT fine-tuning substantially enhances multilingual reasoning, outperforming CoT fine-tuned models. We further demonstrate a strong correlation between reasoning quality (measured through code quality) and answer accuracy, highlighting its potential as a test-time performance improvement heuristic.

## introduction multi-step reasoning is crucial for large language models (llms), enabling them to effectively solve complex tasks, including logical, mathematical, and symbolic problems @xcite . extending this capability to multilingual settings can greatly expand accessibility, allowing diverse multilingual communities to benefit from these advances. however, @xcite ; @xcite showed that llms perform worse in non-english languages due to differences in linguistic structure and training data. this finding highlights the need for approaches that tackle both multi-step reasoning and multilingual challenges. ## experimental setup base llms. we conduct experiments with various base llms, using llama2-7b @xcite 2023) as the foundation for the following variants: i) code-specific variant: codellama-7b @xcite , optimized for code and programming-related tasks. ii) size variant: llama2-13b @xcite , a larger-scale version of llama2. iii) version variant: llama3-8b @xcite , a more recent iteration with enhanced multilingual capabilities. iv) family variant: qwen2.5-7b @xcite , a recent model in another family with strong multilingual performance. oracle llm. to ensure reproducibility, we employ llama3.1-405b instruct @xcite as our oracle model for generating the pot dataset and assessing the quality of the code. evaluation. we evaluate model performance by measuring accuracy on the mgsm @xcite dataset in a zero-shot setting using greedy decoding. the study includes the following languages: english (en), german (de), french (fr), spanish (es), russian (ru), chinese (zh), japanese (ja), thai (th), swahili (sw), and bengali (bn). for cot evaluation, numerical outputs are extracted via regular expressions and compared to labels, following @xcite . for pot evaluation, generated programs are executed in a python interpreter, with outputs compared to labels for accuracy. measures: as outlined in ## conclusion this study explores the effectiveness of programof-thought (pot) prompting for reasoning in cross-and multilingual settings by leveraging the reasoning-execution disentanglement concept. we decompose the problem into two key challenges: (i) aligning multilingual questions with structured reasoning steps and (ii) assessing the impact of reasoning quality on final answer correctness. through systematic experimentation across cross-lingual and multilingual settings, we show that pot finetuning substantially enhances reasoning alignment and generalization, outperforming cot fine-tuning. moreover, we establish a strong correlation between reasoning quality and answer accuracy. by leveraging ice-score-based inference strategies, we enhance performance, particularly in lowresource languages. these findings provide insights into optimizing pot for multilingual reasoning and open avenues for future research on improving reasoning alignment and execution. our findings contribute to a deeper understanding of multilingual pot reasoning, providing insights into fine-tuning strategies and inference-time optimizations. future work can extend this framework to additional reasoning-intensive tasks and explore more advanced alignment techniques to enhance pot's multilingual capabilities.