Large Language Models, the dominant starting point for Natural Language Processing
(NLP) applications, fail at a higher rate for speakers of English dialects other than Standard …

Existing large language models (LLMs) that mainly focus on Standard American English
(SAE) often lead to significantly worse performance when being applied to other English …

Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor
of Standard American English. As a result, speakers of other dialects experience …

Dialect differences caused by regional, social, and economic factors cause performance
discrepancies for many groups of language technology users. Inclusive and equitable …

English Natural Language Understanding (NLU) systems have achieved great
performances and even outperformed humans on benchmarks like GLUE and SuperGLUE …

Language technologies should be judged on their usefulness in real-world use cases. An
often overlooked aspect in natural language processing (NLP) research and evaluation is …

Multidialectal languages can pose challenges for acoustic modeling. Past research has
shown that with a large training corpus but without explicit modeling of inter-dialect …

Aligning Large Language Models (LLMs) is currently the primary method to ensure AI
systems operate in an ethically responsible and socially beneficial manner. Its paradigm …

Currently, natural language processing (NLP) models proliferate language discrimination
leading to potentially harmful societal impacts as a result of biased outcomes. For example …

While standard Estonian is not a low-resourced language, the different dialects of the
language are under-resourced from the point of view of NLP, given that there are no vast …