低代码/无代码人工智能用于机器翻译:为蓝色转换实现生成人工智能的民主化
Mercedes Arguello Casteleiro1, Saihong Li2, Arsenio Andrades Moreno3
1BCS SGAI, UK.
Studies in health technology and informatics
|February 23, 2026
概括
使用生成性AI的神经机器翻译 (MT) 在打破语言障碍方面显示出希望,为像蓝色转型这样的全球举措打破语言障碍. 开源模型虽然可访问,但在翻译关键术语方面表现出适度的质量.
科学领域:
- 星球健康 星球健康
- 水生食物系统 水生食物系统
- 全球卫生倡议全球卫生倡议
背景情况:
- 营养不良和疾病影响人类健康.
- 星球健康将人类的福祉与地球的健康联系起来.
- 蓝色转型旨在改善全球水产食物系统,消除饥饿,提高生活水平.
研究的目的:
- 研究由生成性人工智能 (AI) 驱动的神经机器翻译 (MT) 的潜力.
- 评估人工智能驱动的MT是否可以降低语言障碍,改善获得关键信息的机会.
- 专注于与蓝色转型倡议相关的关键术语.
主要方法:
- 用自动指标和人类判断来评估蓝色转变数据集的MT质量.
- 评估翻译的语义质量 (充分性).
- 采用低代码/无代码人工智能方法,使用开源的Python库和大型语言模型 (LLM),包括多式模式生成人工智能.
主要成果:
- 证实了一种简单,低代码/无代码的AI方法对MT的可行性.
- 专家们在使用Facebook的SeamlessM4T的自动化MT的高质量方面表现出适度的共识.
- 强调了生成AI在翻译专业术语方面的有效性.
结论:
- 研究结果表明,开源大型语言模型 (LLM) 与闭源替代方案 (如OpenAI的GPT4.4) 相比,其性能可能较低.
- 该研究验证了可访问的人工智能工具的使用,以提高全球健康和可持续发展工作中的信息传播.
- 需要进一步的研究,以优化开源的MT复杂的,多语言的举措.
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