Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language
Jipeng Zhang, Jianshu Zhang, Yuanzhe Li, Renjie Pi, Rui Pan, Runtao Liu, Zheng Ziqiang, Tong Zhang
Abstract
Most LLMs universally excel at generating code for high-resource programming languages (HRPLs) like Python, a capability that has become standard due to the abundance of training data. However, they struggle significantly with low-resource programming languages (LRPLs) such as D, exacerbating the digital divide. This gap limits developers using LRPLs from equally benefiting and hinders innovation within underrepresented programming communities. To make matters worse, manually generating data for LRPLs is highly labor intensive and requires expensive expert effort. In this work, we begin by analyzing the NL-PL Gap, where LLMs’ direct-generated LRPL data often suffers from subpar quality due to the misalignment between natural language (NL) instructions and programming language (PL) outputs. To address this issue, we introduce Bridge-Assist Generation, a method to generate LRPL data utilizing LLM’s general knowledge, HRPL proficiency, and in-context learning capabilities. To further maximize the utility of the generated data, we propose Bridged Alignment to obtain Bridge-Coder. To thoroughly evaluate our approach, we select four relatively LRPLs: R, D, Racket, and Bash. Experimental results reveal that Bridge-Coder achieves significant improvements over the original model, with average gains of 18.71 and 10.81 on two comprehensive benchmarks, M-HumanEval and M-MBPP.
BibTeX
@inproceedings{zhang-etal-2025-bridge,
title = "Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language",
author = "Zhang, Jipeng and
Zhang, Jianshu and
Li, Yuanzhe and
Pi, Renjie and
Pan, Rui and
Liu, Runtao and
Ziqiang, Zheng and
Zhang, Tong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.567/",
doi = "10.18653/v1/2025.findings-acl.567",
pages = "10865--10882",
ISBN = "979-8-89176-256-5"
}