77 citations · 319 across the 61 of their papers we have counts for
17 papers · 2 filters
Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages
Poulami Ghosh, Raj Dabre, Pushpak Bhattacharyya
Pre-trained language models (PLMs) are known to be susceptible to perturbations to the input text, but existing works do not explicitly focus on linguistically grounded attacks, wh…
Pralekha: Cross-Lingual Document Alignment for Indic Languages
Sanjay Suryanarayanan, Haiyue Song, Mohammed Safi Ur Rahman Khan +2
Mining parallel document pairs for document-level machine translation (MT) remains challenging due to the limitations of existing Cross-Lingual Document Alignment (CLDA) techniques…
Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages
Ashwin Sankar, Sparsh Jain, Nikhil Narasimhan +6
Speech translation for Indian languages remains a challenging task due to the scarcity of large-scale, publicly available datasets that capture the linguistic diversity and domain…
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…
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan +48
Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate th…
An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models
Nandini Mundra, Aditya Nanda Kishore, Raj Dabre +3
Language Models (LMs) excel in natural language processing tasks for English but show reduced performance in most other languages. This problem is commonly tackled by continually p…
How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?
Pranav Gaikwad, Meet Doshi, Raj Dabre +1
Machine Translation (MT) between linguistically dissimilar languages is challenging, especially due to the scarcity of parallel corpora. Prior works suggest that pivoting through a…
How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages?
Anushka Singh, Ananya B. Sai, Raj Dabre +3
While machine translation evaluation has been studied primarily for high-resource languages, there has been a recent interest in evaluation for low-resource languages due to the in…
A Morphology-Based Investigation of Positional Encodings
Poulami Ghosh, Shikhar Vashishth, Raj Dabre +1
Contemporary deep learning models effectively handle languages with diverse morphology despite not being directly integrated into them. Morphology and word order are closely linked…
Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages
Nathaniel R. Robinson, Raj Dabre, Ammon Shurtz +14
A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole l…
Pretraining Language Models Using Translationese
Meet Doshi, Raj Dabre, Pushpak Bhattacharyya
In this paper, we explore the utility of translationese as synthetic data created using machine translation for pre-training language models (LMs) for low-resource languages (LRLs)…
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…
Airavata: Introducing Hindi Instruction-tuned LLM
Jay Gala, Thanmay Jayakumar, Jaavid Aktar Husain +8
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to…
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities
Settaluri Lakshmi Sravanthi, Meet Doshi, Tankala Pavan Kalyan +3
LLMs have demonstrated remarkable capability for understanding semantics, but they often struggle with understanding pragmatics. To demonstrate this fact, we release a Pragmatics U…
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization
Jaavid Aktar Husain, Raj Dabre, Aswanth Kumar +4
This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages that use non-Roman scripts. We propose an approach that utilizes the romanized…
An Empirical Study of In-context Learning in LLMs for Machine Translation
Pranjal A. Chitale, Jay Gala, Raj Dabre
Recent interest has surged in employing Large Language Models (LLMs) for machine translation (MT) via in-context learning (ICL) (Vilar et al., 2023). Most prior studies primarily f…
Natural Language Processing for Dialects of a Language: A Survey
Aditya Joshi, Raj Dabre, Diptesh Kanojia +4
State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves i…