Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Language models (LMs) have recently shown remarkable performance on reasoning tasks
by explicitly generating intermediate inferences, eg, chain-of-thought prompting. However …

This paper introduces mGPT, a multilingual variant of GPT-3, pretrained on 61 languages
from 25 linguistically diverse language families using Wikipedia and the C4 Corpus. We …

Text preprocessing is not only an essential step to prepare the corpus for modeling but also
a key area that directly affects the natural language processing (NLP) application results. For …

In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has
emerged as a pivotal technique for enhancing model performance by diversifying training …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Graphs are widely used to model interconnected entities and improve downstream
predictions in various real-world applications. However, real-world graphs nowadays are …

The MS MARCO ranking dataset has been widely used for training deep learning models for
IR tasks, achieving considerable effectiveness on diverse zero-shot scenarios. However, this …