Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation

• URL: http://arxiv.org/abs/2508.02497v1
• Date: Mon, 04 Aug 2025 15:07:35 GMT
• Title: Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation
• Authors: Elijah Kayode Adejumo, Brittany Johnson, Mariam Guizani,
• Abstract summary: Large language models (LLMs) have demonstrated remarkable capabilities in software engineering tasks and translations across domains.<n>We evaluate community translation activity and English-to-German translations of 50 files using OpenAI's ChatGPT 4 and Anthropic's Claude.
• Score: 7.742297876120563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While open source communities attract diverse contributors globally, few repositories provide essential documentation in languages other than English. Large language models (LLMs) have demonstrated remarkable capabilities in software engineering tasks and translations across domains. However, little is known about LLM capabilities in translating open-source technical documentation, which mixes natural language, code, URLs, and markdown formatting. To understand the need and potential for LLMs in technical documentation translation, we evaluated community translation activity and English-to-German translations of 50 README files using OpenAI's ChatGPT 4 and Anthropic's Claude. We found scarce translation activity, mostly in larger repositories and community-driven in nature. LLM performance comparison suggests they can provide accurate translations. However, analysis revealed fidelity challenges: both models struggled to preserve structural components (e.g., hyperlinks) and exhibited formatting inconsistencies. These findings highlight both promise and challenges of LLM-assisted documentation internationalization. As a first step toward translation-aware continuous integration pipelines, we introduce TRIFID, an early-stage translation fidelity scoring framework that automatically checks how well translations preserve code, links, and formatting. Our efforts provide a foundation for automated LLM-driven support for creating and maintaining open source documentation.

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