迈向全球人工智能包容性：一个大规模多语言术语数据集（GIST）
Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)
AI总结：
本文提出大规模多语言AI术语数据集GIST，结合大语言模型提取与人工翻译构建5000个术语的五种语言版本，并通过无需重训练的后翻译优化提升翻译质量，以促进全球AI研究的包容性与协作。
摘要：
机器翻译领域已取得显著进展，但特定领域的术语翻译，尤其是人工智能领域的术语翻译，仍然具有挑战性。我们引入了GIST，一个大规模多语言人工智能术语数据集，包含从2000年至2023年顶级人工智能会议论文中提取的5000个术语。这些术语被翻译成阿拉伯语、中文、法语、日语和俄语，采用了一个混合框架，该框架结合了用于提取的大语言模型（LLM）和用于翻译的人类专业知识。该数据集的质量与现有资源进行了基准对比，通过众包评估展示了卓越的翻译准确性。GIST被集成到翻译工作流程中，使用无需重新训练的后翻译优化方法，其中大语言模型提示持续提高了BLEU和COMET分数。在ACL Anthology平台上的网络演示突出了其实际应用，展示了非英语使用者可访问性的改善。这项工作旨在解决人工智能术语资源中的关键缺口，并促进人工智能研究中的全球包容性与协作。我们的数据位于https://huggingface.co/datasets/Jerry999/multilingual-terminology。
英文摘要：
The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. We introduce GIST, a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms are translated into Arabic, Chinese, French, Japanese, and Russian using a hybrid framework that combines LLMs for extraction with human expertise for translation. The dataset's quality is benchmarked against existing resources, demonstrating superior translation accuracy through crowdsourced evaluation. GIST is integrated into translation workflows using post-translation refinement methods that require no retraining, where LLM prompting consistently improves BLEU and COMET scores. A web demonstration on the ACL Anthology platform highlights its practical application, showcasing improved accessibility for non-English speakers. This work aims to address critical gaps in AI terminology resources and fosters global inclusivity and collaboration in AI research. Our data is at https://huggingface.co/datasets/Jerry999/multilingual-terminology
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
