# InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translation arXiv: https://arxiv.org/abs/2411.01063 ยท HTML: https://mlsanity.com/paper/2411.01063/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 40 references that are in the map. - First posted on arXiv 1 Nov 2024. ## Builds on (most cited first, 23 of 23) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023) - [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023) - [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023) - [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022) - [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020) - [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024) - [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020) - [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021) - [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023) - [CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code](https://mlsanity.com/paper/2302.05527/) (2023) - [Tree-to-tree Neural Networks for Program Translation](https://mlsanity.com/paper/1802.03691/) (2018) - [HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization](https://mlsanity.com/paper/2402.16694/) (2024) - [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021) - [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021) - [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024) - [McEval: Massively Multilingual Code Evaluation](https://mlsanity.com/paper/2406.07436/) (2024) - [Code Translation with Compiler Representations](https://mlsanity.com/paper/2207.03578/) (2022) - [Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages](https://mlsanity.com/paper/1909.09524/) (2019) - [On the Evaluation of Neural Code Translation: Taxonomy and Benchmark](https://mlsanity.com/paper/2308.08961/) (2023) - [Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example](https://mlsanity.com/paper/2402.07138/) (2024) - [Output Format Biases in the Evaluation of Large Language Models for Code Translation](https://mlsanity.com/paper/2403.17214/) (2024) ## Built on by (most cited first, 16 of 16) - [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025) - [Enhancing LLM Performance Through Debate: An Empirical Study on Multi-Agent Debate for Coding Tasks](https://mlsanity.com/paper/2503.12029/) (2025) - [EffiReasonTrans: RL-Optimized Reasoning for Code Translation](https://mlsanity.com/paper/2510.18863/) (2025) - [Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Features](https://mlsanity.com/paper/2502.17749/) (2025) - [Mix-of-Language-Experts Architecture for Multilingual Programming](https://mlsanity.com/paper/2506.18923/) (2025) - [Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation](https://mlsanity.com/paper/2509.19918/) (2025) - [Large Language Models for Multilingual Code Intelligence: A Survey](https://mlsanity.com/paper/2604.25960/) (2026) - [SPDZCoder: Combining Expert Knowledge with LLMs for Generating Privacy-Computing Code](https://mlsanity.com/paper/2501.00363/) (2024) - [BabelCoder: Agentic Code Translation with Specification Alignment](https://mlsanity.com/paper/2512.06902/) (2025) - [Learning Bug Context for PyTorch-to-JAX Translation with LLMs](https://mlsanity.com/paper/2510.09898/) (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) - [Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning](https://mlsanity.com/paper/2606.19988/) (2026) - [Identifying Latent Declarative Representations of Code for Assisting Repository Migration](https://mlsanity.com/paper/2608.23619/) (2026) - [Legacy System Modernization with Coding Agents: A Case Study](https://mlsanity.com/paper/2608.28972/) (2026) - [Predicting Program Exit Code with LLMs and Programming Language Semantics](https://mlsanity.com/paper/2609.00579/) (2026) ## Abstract (arXiv) Code translation aims to convert a program from one programming language (PL) to another. This long-standing software engineering task is crucial for modernizing legacy systems, ensuring cross-platform compatibility, enhancing performance, and more. However, automating this process remains challenging due to many syntactic and semantic differences between PLs. Recent studies show that even advanced techniques such as large language models (LLMs), especially open-source LLMs, still struggle with the task. Currently, code LLMs are trained with source code from multiple programming languages, thus presenting multilingual capabilities. In this paper, we investigate whether such multilingual capabilities can be harnessed to enhance code translation. To achieve this goal, we introduce InterTrans, an LLM-based automated code translation approach that, in contrast to existing approaches, leverages intermediate translations across PLs to bridge the syntactic and semantic gaps between source and target PLs. InterTrans contains two stages. It first utilizes a novel Tree of Code Translation (ToCT) algorithm to plan transitive intermediate translation sequences between a given source and target PL, then validates them in a specific order. We evaluate InterTrans with three open LLMs on three benchmarks (i.e., CodeNet, HumanEval-X, and TransCoder) involving six PLs. Results show an absolute improvement between 18.3% to 43.3% in Computation Accuracy (CA) for InterTrans over Direct Translation with 10 attempts. The best-performing variant of InterTrans (with Magicoder LLM) achieved an average CA of 87.3%-95.4% on three benchmarks.