@inproceedings{chen-etal-2026-beyond-code,
title = "Beyond Code Pairs: Dialogue-Based Data Generation for {LLM} Code Translation",
author = "Chen, Le and
Xu, Nuo and
Chen, Winson and
Lei, Bin and
Lin, Pei-Hung and
Zhou, Dunzhi and
Thakur, Rajeev and
Ding, Caiwen and
Jannesari, Ali and
Liao, Chunhua",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1557/",
pages = "33781--33803",
ISBN = "979-8-89176-390-6",
abstract = "Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce. We present an automated dataset generation pipeline featuring a dual-LLM Questioner{--}Solver design that incorporates external knowledge from compilers and runtime feedback. Beyond traditional source{--}target code pair datasets, our approach additionally generates (1) verified translations with unit tests for assessing functional consistency, and (2) multi-turn dialogues that capture the reasoning process behind translation refinement. Applied to Fortran{\textrightarrow}C++ and C++{\textrightarrow}CUDA, the pipeline yields 3.64k and 3.93k dialogues, respectively. Fine-tuning on this data yields dramatic improvements in functional correctness, boosting unit test success rates by over 56{\%} on the challenging C++-to-CUDA task. We show that the generated data enables a 7B open-weight model to significantly outperform larger proprietary systems on key metrics like compilation success."
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%0 Conference Proceedings
%T Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation
%A Chen, Le
%A Xu, Nuo
%A Chen, Winson
%A Lei, Bin
%A Lin, Pei-Hung
%A Zhou, Dunzhi
%A Thakur, Rajeev
%A Ding, Caiwen
%A Jannesari, Ali
%A Liao, Chunhua
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chen-etal-2026-beyond-code
%X Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce. We present an automated dataset generation pipeline featuring a dual-LLM Questioner–Solver design that incorporates external knowledge from compilers and runtime feedback. Beyond traditional source–target code pair datasets, our approach additionally generates (1) verified translations with unit tests for assessing functional consistency, and (2) multi-turn dialogues that capture the reasoning process behind translation refinement. Applied to Fortran→C++ and C++→CUDA, the pipeline yields 3.64k and 3.93k dialogues, respectively. Fine-tuning on this data yields dramatic improvements in functional correctness, boosting unit test success rates by over 56% on the challenging C++-to-CUDA task. We show that the generated data enables a 7B open-weight model to significantly outperform larger proprietary systems on key metrics like compilation success.
%U https://aclanthology.org/2026.acl-long.1557/
%P 33781-33803
Markdown (Informal)
[Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation](https://aclanthology.org/2026.acl-long.1557/) (Chen et al., ACL 2026)
ACL
- Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, and Chunhua Liao. 2026. Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33781–33803, San Diego, California, United States. Association for Computational Linguistics.