52 citations · 181 across the 16 of their papers we have counts for
16 papers
Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs
Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Dilip Venkatesh +3
Evaluating machine-generated text remains a significant challenge in NLP, especially for non-English languages. Current methodologies, including automated metrics, human assessment…
PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting
Liam Hebert, Krishna Sayana, Ambarish Jash +5
Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preference…
Finding Blind Spots in Evaluator LLMs with Interpretable Checklists
Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Sshubam Verma +1
Large Language Models (LLMs) are increasingly relied upon to evaluate text outputs of other LLMs, thereby influencing leaderboards and development decisions. However, concerns pers…
IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages
Mohammed Safi Ur Rahman Khan, Priyam Mehta, Ananth Sankar +9
Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. O…
User Embedding Model for Personalized Language Prompting
Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash +2
Modeling long histories plays a pivotal role in enhancing recommendation systems, allowing to capture user's evolving preferences, resulting in more precise and personalized recomm…
A Comprehensive Analysis of Adapter Efficiency
Nandini Mundra, Sumanth Doddapaneni, Raj Dabre +3
Adapters have been positioned as a parameter-efficient fine-tuning (PEFT) approach, whereby a minimal number of parameters are added to the model and fine-tuned. However, adapters…
Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages
Rahul Aralikatte, Ziling Cheng, Sumanth Doddapaneni +1
We present Vārta, a large-scale multilingual dataset for headline generation in Indic languages. This dataset includes 41.8 million news articles in 14 different Indic languages (a…
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 (…
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages
Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni +4
We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. The dataset contains mor…
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages
Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh +4
Building Natural Language Understanding (NLU) capabilities for Indic languages, which have a collective speaker base of more than one billion speakers is absolutely crucial. In thi…
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages
Kaushal Santosh Bhogale, Abhigyan Raman, Tahir Javed +4
End-to-end (E2E) models have become the default choice for state-of-the-art speech recognition systems. Such models are trained on large amounts of labelled data, which are often n…
A Survey of Adversarial Defences and Robustness in NLP
Shreya Goyal, Sumanth Doddapaneni, Mitesh M. Khapra +1
In the past few years, it has become increasingly evident that deep neural networks are not resilient enough to withstand adversarial perturbations in input data, leaving them vuln…
Towards Building ASR Systems for the Next Billion Users
Tahir Javed, Sumanth Doddapaneni, Abhigyan Raman +5
Recent methods in speech and language technology pretrain very LARGE models which are fine-tuned for specific tasks. However, the benefits of such LARGE models are often limited to…
Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss
Debapriya Tula, Shreyas MS, Viswanatha Reddy +5
Over the past decade, we have seen exponential growth in online content fueled by social media platforms. Data generation of this scale comes with the caveat of insurmountable offe…
A Primer on Pretrained Multilingual Language Models
Sumanth Doddapaneni, Gowtham Ramesh, Mitesh M. Khapra +2
Multilingual Language Models (\MLLMs) such as mBERT, XLM, XLM-R, \textit{etc.} have emerged as a viable option for bringing the power of pretraining to a large number of languages.…
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj +15
We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between Englis…