Context-Aware Large Language Models for Multilingual Understanding
Year: 2025
Type: article
Abstract: Multilingual large language models (LLMs) have demonstrated strong performance in cross-lingual tasks; however, their ability to incorporate context across diverse languages remains underexplored. This paper proposes a Context-Aware Multilingual Transformer (CAMT) architecture that integrates dynamic context routing, semantic alignment layers, and cultural knowledge embeddings to enhance multilingual understanding. Experiments conducted using the FLORES-200 and XNLI datasets show that CAMT improves context retention by 12.4%, cross-lingual consistency by 9.8%, and cultural disambiguation by 7.1% compared to baseline mT5 and XLM-R models. Results highlight the importance of contextual cues in multilingual communication and underline the potential for building globally robust LLMs.
Institution: Sigma University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7119997381
FWCI:
Cites:
Cited by:
Related to: