1 citations · 2 across the 8 of their papers we have counts for
8 papers
Lost in Reordering: Structural Sensitivity of Multilingual LLMs under Semantics-Preserving Perturbations
Karthika Nhayakkat, Rajat Verma, Maharaj Brahma +4
Large Language Models (LLMs) demonstrate strong multilingual reasoning performance, yet their robustness to semantics-preserving structural variation remains underexplored, particu…
Improving Video Question Answering through query-based frame selection
Himanshu Patil, Geo Jolly, Ramana Raja Buddala +2
Video Question Answering (VideoQA) models enhance understanding and interaction with audiovisual content, making it more accessible, searchable, and useful for a wide range of fiel…
AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda
Sravan Gorugantu, Mohd Nauman, Sravan Gvm +8
Current large language models excel at broad, general-purpose tasks, but consistently underperform when exposed to highly specialized domains that require deep cultural, linguistic…
The Art of Breaking Words: Rethinking Multilingual Tokenizer Design
Aamod Thakur, Ajay Nagpal, Atharva Savarkar +7
While model architecture and training objectives are well-studied, tokenization, particularly in multilingual contexts, remains a relatively neglected aspect of Large Language Mode…
Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering
Abhay Vijayvargia, Ajay Nagpal, Kundeshwar Pundalik +5
Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy. To address this gap in accessibility, this pap…
Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights
Maharaj Brahma, N J Karthika, Rajat Verma +4
Tokenization plays a pivotal role in NLP and is fundamental to training language models. However, existing tokenizers are often skewed towards high-resource languages, limiting the…
MorphTok: Morphologically Grounded Tokenization for Indian Languages
Maharaj Brahma, N J Karthika, Atul Singh +5
Tokenization is a crucial step in NLP, especially with the rise of large language models (LLMs), impacting downstream performance, computational cost, and efficiency. Existing LLMs…
Semantically Cohesive Word Grouping in Indian Languages
N J Karthika, Adyasha Patra, Nagasai Saketh Naidu +3
Indian languages are inflectional and agglutinative and typically follow clause-free word order. The structure of sentences across most major Indian languages are similar when thei…