Diverse Corpus

Diverse corpora, collections of text and other data from varied sources and languages, are crucial for training robust and generalizable natural language processing (NLP) models. Current research focuses on developing and evaluating these corpora, particularly for under-resourced languages, and on improving model training techniques to effectively leverage their heterogeneity, including methods like source prompting and retrieval augmentation. This work is significant because it addresses biases inherent in homogenous datasets and enables the development of NLP tools applicable across diverse linguistic and cultural contexts, impacting fields ranging from machine translation to mental health detection.