# Exploring Large Language Models for Translating Romanian Computational Problems into English

arXiv: https://arxiv.org/abs/2501.05601 · HTML: https://mlsanity.com/paper/2501.05601/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 22 of 22)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [The Effect of Sampling Temperature on Problem Solving in Large Language Models](https://mlsanity.com/paper/2402.05201/) (2024)
- [TACO: Topics in Algorithmic COde generation dataset](https://mlsanity.com/paper/2312.14852/) (2023)
- [Is Temperature the Creativity Parameter of Large Language Models?](https://mlsanity.com/paper/2405.00492/) (2024)
- [How Does Quantization Affect Multilingual LLMs?](https://mlsanity.com/paper/2407.03211/) (2024)
- [Competition-Level Problems are Effective LLM Evaluators](https://mlsanity.com/paper/2312.02143/) (2023)
- [AutoCoder: Enhancing Code Large Language Model with textscAIEV-Instruct](https://mlsanity.com/paper/2405.14906/) (2024)
- [OpenLLM-Ro -- Technical Report on Open-source Romanian LLMs](https://mlsanity.com/paper/2405.07703/) (2024)
- [QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models](https://mlsanity.com/paper/2307.03738/) (2023)
- [Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems](https://mlsanity.com/paper/2310.05791/) (2023)
- [RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian](https://mlsanity.com/paper/2402.13222/) (2024)
- [RoMath: A Mathematical Reasoning Benchmark in Romanian](https://mlsanity.com/paper/2409.11074/) (2024)

## Built on by (most cited first, 2 of 2)

- [Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests](https://mlsanity.com/paper/2506.05990/) (2025)
- [From Struggle (06-2024) to Mastery (02-2025) LLMs Conquer Advanced Algorithm Exams and Pave the Way for Editorial Generation](https://mlsanity.com/paper/2506.04965/) (2025)

## Abstract (arXiv)

Recent studies have suggested that large language models (LLMs) underperform on mathematical and computer science tasks when these problems are translated from Romanian into English, compared to their original Romanian format. Accurate translation is critical for applications ranging from automatic translations in programming competitions to the creation of high-quality educational materials, as well as minimizing errors or fraud in human translations. This study shows that robust large language models (LLMs) can maintain or even enhance their performance in translating less common languages when given well-structured prompts. Our findings suggest that LLMs, with appropriate supervision, can be reliably used for the automatic translation of IOI (International Olympiad in Informatics)-style tasks. We evaluate several translation methods across multiple LLMs, including OpenRoLLM, Llama 3.1 8B, Llama 3.2 3B and GPT-4o, assessing their translation accuracy and performance stability through repeated runs. Additionally, we augment the OJI (Romanian County-Level Informatics Olympiad) Romanian dataset with accurate English translations, enhancing its utility for future LLM training and evaluation. Through detailed syntactic and semantic analyses, we confirm that with human oversight, LLMs can serve as a viable solution for multilingual problem-solving. We also compare the translation quality of LLMs against human translators, as evaluated by a certified expert, underscoring the potential of LLMs in realworld scenarios.