What question did this study set out to answer?

The goal is to enhance multilingual machine translation by addressing contextual ambiguity through advanced models.

April 18, 2026Open Access

Enhancing Multilingual Machine Translation Using Context Aware Large Language Models

Key Points

The goal is to enhance multilingual machine translation by addressing contextual ambiguity through advanced models.
Integrated transformer-based architectures with contextual embeddings
Evaluated using BLEU, METEOR, and qualitative human assessments
Focused on handling long-range dependencies in translations
Context-aware LLMs outperformed traditional models in translation tasks
Improved semantic consistency and translation fluency observed
Enhanced performance noted in low-resource languages

Abstract

Multilingual is a critical component of global communication systems. Despite significant (NMT), contextual ambiguity, low-resource language, domain adaptation persist. Enhanced by leveraging context-aware (LLMs). By integrating transformer-based architectures with contextual embeddings, the proposed approach improves semantic consistency, translation fluency, and cross-lingual transfer learning. The study BLEU and METEOR while also considering qualitative human evaluation. Results indicate that context-aware LLMs significantly outperform traditional models in handling long-range dependencies and multilingual tasks. The paper concludes with a discussion on limitations and future research directions. \\

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