# Unraveling the Potential of Large Language Models in Code Translation: How Far Are We?

arXiv: https://arxiv.org/abs/2410.09812 · HTML: https://mlsanity.com/paper/2410.09812/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2024.

## Built on by (most cited first, 17 of 17)

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- [AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion](https://mlsanity.com/paper/2601.19697/) (2026)
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- [FreshBrew: A Benchmark for Evaluating AI Agents on Java Code Migration](https://mlsanity.com/paper/2510.04852/) (2025)
- [EffiReasonTrans: RL-Optimized Reasoning for Code Translation](https://mlsanity.com/paper/2510.18863/) (2025)
- [Function-to-Style Guidance of LLMs for Code Translation](https://mlsanity.com/paper/2507.11083/) (2025)
- [Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation](https://mlsanity.com/paper/2501.16050/) (2025)
- [DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning](https://mlsanity.com/paper/2601.20615/) (2026)
- [PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents](https://mlsanity.com/paper/2607.18859/) (2026)
- [TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework](https://mlsanity.com/paper/2510.17163/) (2025)
- [Automated Snippet-Alignment Data Augmentation for Code Translation](https://mlsanity.com/paper/2510.15004/) (2025)
- [Revisiting the Role of Natural Language Code Comments in Code Translation](https://mlsanity.com/paper/2601.16661/) (2026)
- [Bootstrapping Niche Multilingual Code Translation via Reinforcement Learning with Execution-Based Verifiable Supervision](https://mlsanity.com/paper/2608.13854/) (2026)

## Abstract (arXiv)

While large language models (LLMs) exhibit state-of-the-art performance in various tasks, recent studies have revealed their struggle for code translation. This is because they haven't been extensively pre-trained with parallel multilingual code, which code translation heavily depends on. Moreover, existing benchmarks only cover a limited subset of common programming languages, and thus cannot reflect the full potential of LLMs in code translation. In this paper, we conduct a large-scale empirical study to exploit the capabilities and incapabilities of LLMs in code translation tasks. We first craft a novel benchmark called PolyHumanEval by extending HumanEval to a multilingual benchmark of 14 languages. With PolyHumanEval, we then perform over 110,000 translations with bleeding-edge code LLMs. The result shows LLMs' suboptimal performance on Python to other languages and the negligible impact of widely adopted LLM optimization techniques such as conventional pre-training and instruction tuning on code translation. To further uncover the potential of LLMs in code translation, we propose two methods: (1) intermediary translation which selects an intermediary language between the source and target ones; and (2) self-training which fine-tunes LLMs on self-generated parallel data. Evaluated with CodeLlama-13B, our approach yields an average improvement of 11.7% computation accuracy on Python-to-other translations. Notably, we interestingly find that Go can serve as a lingua franca for translating between any two studied languages.