37 citations · 53 across the 14 of their papers we have counts for
13 papers · 1 filter
UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages
Pranjal A. Chitale, Varun Gumma, Sanchit Ahuja +4
Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multi…
Contamination Report for Multilingual Benchmarks
Sanchit Ahuja, Varun Gumma, Sunayana Sitaram
Benchmark contamination refers to the presence of test datasets in Large Language Model (LLM) pre-training or post-training data. Contamination can lead to inflated scores on bench…
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings
Varun Gumma, Ananditha Raghunath, Mohit Jain +1
Assessing the capabilities and limitations of large language models (LLMs) has garnered significant interest, yet the evaluation of multiple models in real-world scenarios remains…
Towards Inducing Long-Context Abilities in Multilingual Neural Machine Translation Models
Varun Gumma, Pranjal A. Chitale, Kalika Bali
Neural Machine Translation (NMT) models have traditionally used Sinusoidal Positional Embeddings (PEs), which often struggle to capture long-range dependencies and are inefficient…
PARIKSHA: A Large-Scale Investigation of Human-LLM Evaluator Agreement on Multilingual and Multi-Cultural Data
Ishaan Watts, Varun Gumma, Aditya Yadavalli +3
Evaluation of multilingual Large Language Models (LLMs) is challenging due to a variety of factors -- the lack of benchmarks with sufficient linguistic diversity, contamination of…
Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology
Rishav Hada, Safiya Husain, Varun Gumma +8
Existing research in measuring and mitigating gender bias predominantly centers on English, overlooking the intricate challenges posed by non-English languages and the Global South…
METAL: Towards Multilingual Meta-Evaluation
Rishav Hada, Varun Gumma, Mohamed Ahmed +2
With the rising human-like precision of Large Language Models (LLMs) in numerous tasks, their utilization in a variety of real-world applications is becoming more prevalent. Severa…
MAFIA: Multi-Adapter Fused Inclusive LanguAge Models
Prachi Jain, Ashutosh Sathe, Varun Gumma +2
Pretrained Language Models (PLMs) are widely used in NLP for various tasks. Recent studies have identified various biases that such models exhibit and have proposed methods to corr…
MunTTS: A Text-to-Speech System for Mundari
Varun Gumma, Rishav Hada, Aditya Yadavalli +4
We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work addresses the gap in lin…
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma +8
There has been a surge in LLM evaluation research to understand LLM capabilities and limitations. However, much of this research has been confined to English, leaving LLM building…
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?
Rishav Hada, Varun Gumma, Adrian de Wynter +5
Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top , remains inadequate due t…
IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages
Jay Gala, Pranjal A. Chitale, Raghavan AK +11
India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (…
An Empirical Study of Leveraging Knowledge Distillation for Compressing Multilingual Neural Machine Translation Models
Varun Gumma, Raj Dabre, Pratyush Kumar
Knowledge distillation (KD) is a well-known method for compressing neural models. However, works focusing on distilling knowledge from large multilingual neural machine translation…