activity
20242026
collaborators

6 papers

cs.CL2026

L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models

Hariom Ingle, Ronit Ghode, Ishwari Gondkar +2

Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing. Despite Marathi being spoken by over 83 mil…

cs.CL2026

IndicGuard: A Multilingual Safety Guard Model and Dataset for Indic Languages

Parth Bramhecha, Smit Deshmukh, Sairaj Bodhale +2

As Large Language Models (LLMs) achieve widespread integration across diverse linguistic landscapes, ensuring their safety and alignment with regional normative values remains a cr…

cs.CL2025

Better To Ask in English? Evaluating Factual Accuracy of Multilingual LLMs in English and Low-Resource Languages

Pritika Rohera, Chaitrali Ginimav, Gayatri Sawant +1

Multilingual Large Language Models (LLMs) have demonstrated significant effectiveness across various languages, particularly in high-resource languages such as English. However, th…

cs.CL2025

L3Cube-IndicHeadline-ID: A Dataset for Headline Identification and Semantic Evaluation in Low-Resource Indian Languages

Nishant Tanksale, Tanmay Kokate, Darshan Gohad +2

Semantic evaluation in low-resource languages remains a major challenge in NLP. While sentence transformers have shown strong performance in high-resource settings, their effective…

cs.CL2025

IndicSQuAD: A Comprehensive Multilingual Question Answering Dataset for Indic Languages

Sharvi Endait, Ruturaj Ghatage, Aditya Kulkarni +2

The rapid progress in question-answering (QA) systems has predominantly benefited high-resource languages, leaving Indic languages largely underrepresented despite their vast nativ…

cs.CL2024

L3Cube-IndicQuest: A Benchmark Question Answering Dataset for Evaluating Knowledge of LLMs in Indic Context

Pritika Rohera, Chaitrali Ginimav, Akanksha Salunke +2

Large Language Models (LLMs) have made significant progress in incorporating Indic languages within multilingual models. However, it is crucial to quantitatively assess whether the…