Parameswari Krishnamurthy

Santham : A Curated Sanskrit–Tamil Dataset with Anvaya and Segmentation for Building and Evaluating Machine Translation

SurakshaEval: An Indic Safety Benchmark for Multilingual LLMs

SurakshaEval is introduced, a novel safety benchmark composed of human-written prompts spanning real-world scenarios, explicitly designed for ten major Indian languages - Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, Tamil, and Telugu - along with English.

What Tokens are Learned when Tokenization is Optimized Jointly with Language Modeling?

This work analyzes what tokens are learned when tokenization is jointly optimized with language modeling, and finds tokenizer-free approaches optimize for contextual and computational efficiency rather than strict morphological structure, resulting in fundamentally different yet effective vocabularies for downstream NLP.