Parul Chopra — accepted AI-conference papers

2025

Scaling Laws for Multilingual Language Models

Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra +3

ACL 2025finding

We propose a novel scaling law for general-purpose decoder-only language models (LMs) trained on multilingual data, tackling the problem of balancing languages during multilingual pretraining. A primary challenge in studying multilingual scaling is the difficulty of analyzing individual language per…

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2021

Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching

Parul Chopra, Sai Krishna Rallabandi, Alan W Black, Khyathi Raghavi Chandu

EMNLP 2021finding

Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. The primary reasons behind this are: (1) minimal efforts in leveraging large pretrained multilingual models, and (2) the l…

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