arXiv:2512.06734v1 Announce Type: new 
Abstract: Transfer Learning (TL) has accelerated the rapid development and availability of large language models (LLMs) for mainstream natural language processing (NLP) use cases. However, training and deploying such gigantic LLMs in resource-constrained, real-world healthcare situations remains challenging. This study addresses the limited support available to visually impaired users and speakers of low-resource languages such as Hindi who require medical assistance in rural environments. We propose PDFTEMRA (Performant Distilled Frequency Transformer Ensemble Model with Random Activations), a compact transformer-based architecture that integrates model distillation, frequency-domain modulation, ensemble learning, and randomized activation patterns to reduce computational cost while preserving language understanding performance. The model is trained and evaluated on medical question-answering and consultation datasets tailored to Hindi and accessibility scenarios, and its performance is compared against standard NLP state-of-the-art model baselines. Results demonstrate that PDFTEMRA achieves comparable performance with substantially lower computational requirements, indicating its suitability for accessible, inclusive, low-resource medical NLP applications.

تقدم دراسة جديدة نموذج PDFTEMRA، وهو هيكل مضغوط قائم على المحولات مصمم لتحسين المساعدة الطبية للمستخدمين ذوي الإعاقة البصرية والمتحدثين باللغات ذات الموارد المحدودة مثل الهندية في بيئات الرعاية الصحية الريفية. يستفيد هذا النموذج من تقنيات التعلم بالنقل والتعلم الجماعي لتحسين الأداء مع تقليل التكاليف الحاسوبية.

Un nuevo estudio presenta PDFTEMRA, una arquitectura compacta basada en transformadores diseñada para mejorar la asistencia médica a usuarios con discapacidad visual y hablantes de lenguas de bajos recursos como el hindi en entornos de atención médica rural. Este modelo aprovecha el aprendizaje por transferencia y técnicas de aprendizaje en conjunto para optimizar el rendimiento mientras minimiza los costos computacionales.

Une nouvelle étude présente PDFTEMRA, une architecture compacte basée sur des transformateurs conçue pour améliorer l'assistance médicale aux utilisateurs malvoyants et aux locuteurs de langues à faibles ressources comme l'hindi dans les environnements de soins de santé ruraux. Ce modèle utilise des techniques d'apprentissage par transfert et d'apprentissage en ensemble pour optimiser les performances tout en minimisant les coûts computationnels.

A new study introduces PDFTEMRA, a compact transformer-based architecture designed to enhance medical assistance for visually impaired users and speakers of low-resource languages like Hindi in rural healthcare settings. This model leverages transfer learning and ensemble learning techniques to optimize performance while minimizing computational costs.

A Patient-Doctor-NLP-System to contest inequality for less privileged

arXiv:2510.06249v4 Announce Type: replace 
Abstract: The 2025 Multimodal Models for Low-Resource Contexts and Social Impact (MMLoSo) Language Challenge addresses one of India's most pressing linguistic gaps: the lack of resources for its diverse low-resource languages (LRLs). In this study, we investigate whether enforcing cross-lingual similarity in specific internal layers of a decoder-only multilingual large language model (LLM) can improve translation quality from LRL to high-resource language (HRL). Specifically, we combine Centered Kernel Alignment (CKA), a similarity metric that encourages representations of different languages to align, with REPINA, a regularization method that constrains parameter updates to remain close to the pretrained model, into a joint method we call TRepLiNa. In this research project, we experiment with zero-shot, few-shot, and fine-tuning settings using Aya-23 8B with QLoRA across MMLoSo shared task language pairs (Mundari, Santali, Bhili) with Hindi/English pivots. Our results show that aligning mid-level layers using TRepLiNa (CKA+REPINA) is a low-cost, practical approach to improving LRL translation, especially in data-scarce settings.

تم تقديم طريقة TRepLiNa، التي تجمع بين محاذاة النواة المركزية (CKA) وREPINA، لتحسين الترجمة الآلية للغات ذات الموارد المنخفضة، وخاصة للغات هندية مثل مونداري، سانتالي، وبهيلي، باستخدام نموذج Aya-23 8B. يهدف هذا النهج إلى تحسين جودة الترجمة من اللغات ذات الموارد المنخفضة إلى اللغات ذات الموارد العالية مثل الهندية والإنجليزية.

Se ha introducido el método TRepLiNa, que combina la Alineación de Núcleo Centrado (CKA) y REPINA, para mejorar la traducción automática de idiomas de bajos recursos, especialmente para idiomas indios como Mundari, Santali y Bhili, utilizando el modelo Aya-23 8B. Este enfoque busca mejorar la calidad de la traducción de idiomas de bajos recursos a idiomas de altos recursos como el hindi y el inglés.

La méthode TRepLiNa, qui combine l'Alignement de Noyau Centré (CKA) et REPINA, a été introduite pour améliorer la traduction automatique des langues à faibles ressources, en particulier pour des langues indiennes comme le Mundari, le Santali et le Bhili, en utilisant le modèle Aya-23 8B. Cette approche vise à améliorer la qualité de la traduction des langues à faibles ressources vers des langues à fortes ressources comme l'hindi et l'anglais.

The TRepLiNa method, which combines Centered Kernel Alignment (CKA) and REPINA, has been introduced to enhance low-resource machine translation, particularly for Indian languages like Mundari, Santali, and Bhili, using the Aya-23 8B model. This approach aims to improve translation quality from low-resource languages to high-resource languages such as Hindi and English.

A Patient-Doctor-NLP-System to contest inequality for less privileged

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