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Low-Code/No-Code AI for Machine Translation: Democratising Generative AI for Blue Transformation.
Mercedes Arguello Casteleiro1, Saihong Li2, Arsenio Andrades Moreno3
1BCS SGAI, UK.
Studies in Health Technology and Informatics
|February 23, 2026
Summary
Neural machine translation (MT) using generative AI shows promise in breaking down language barriers for global initiatives like the Blue Transformation. Open-source models, while accessible, demonstrated moderate quality in translating key terms.
Area of Science:
- Planetary Health
- Aquatic Food Systems
- Global Health Initiatives
Background:
- Malnutrition and disease impact human health.
- Planetary Health connects human well-being with Earth's health.
- The Blue Transformation aims to improve global aquatic food systems, eradicate hunger, and enhance living standards.
Purpose of the Study:
- Investigate the potential of neural machine translation (MT) powered by generative Artificial Intelligence (AI).
- Assess if AI-driven MT can reduce language barriers and improve access to critical information.
- Focus on key terms related to the Blue Transformation initiative.
Main Methods:
- Evaluated MT quality for a Blue Transformation dataset using automatic metrics and human judgment.
- Assessed semantic quality (adequacy) of translations.
- Employed a low-code/no-code AI approach with open-source Python libraries and Large Language Models (LLMs), including multimodal generative AI.
Main Results:
- Confirmed the viability of a simple, low-code/no-code AI approach for MT.
- Demonstrated moderate agreement among experts on the high quality of automated MT using Facebook's SeamlessM4T.
- Highlighted the effectiveness of generative AI in translating specialized terminology.
Conclusions:
- Findings suggest open-source Large Language Models (LLMs) may have lower performance compared to closed-source alternatives like OpenAI's GPT4.
- The study validates the use of accessible AI tools for enhancing information dissemination in global health and sustainability efforts.
- Further research is needed to optimize open-source MT for complex, multilingual initiatives.
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