Computer science has a language problem--and we are not alluding to programming languages. Many prevalent, flawed views about natural human language are limiting who is in computer science and what people can accomplish with the technology we build. To start, computer science centers around the English language, and that produces technologies that work poorly for many people. As Manuel Pérez-Quiñonesa points out, when developers make assumptions about English as the default language, navigating digital device interfaces can be frustrating, even for a professional computer scientist fluent in English such as Pérez-Quiñones. Poor multilingual or character-encoding support, incorrect cultural norms baked into software, and so on--these challenges confront users all over the world.

Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities and have excelled in a variety of natural language processing (NLP)tasks in recent years. Despite the fact that most LLMs are trained predominantly in English, multiple studies have demonstrated their comparative performance in many other languages. However, fundamental questions persist regarding how LLMs acquire their multi-lingual abilities and how performance varies across different languages. These inquiries are crucial for the study of LLMs since users and researchers often come from diverse language backgrounds, potentially influencing their utilization and interpretation of LLMs' results. In this work, we propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings. We investigate the phenomenon of across-language generalizations in LLMs, wherein insufficient multi-lingual training data leads to advanced multi-lingual capabilities. To accomplish this, we employ a novel back-translation-based prompting method. The results show that GPT exhibits highly translating-like behaviour in multilingual settings.

The Multilingual Semantic Web has been in focus for over a decade. Multilingualism in Linked Data and RDF has shown substantial adoption, but this is unclear for ontologies since the last review 15 years ago. One of the design goals for OWL was internationalisation, with the aim that an ontology is usable across languages and cultures. Much research to improve on multilingual ontologies has taken place in the meantime, and presumably multilingual linked data could use multilingual ontologies. Therefore, this review seeks to (i) elucidate and compare the modelling options for multilingual ontologies, (ii) examine extant ontologies for their multilingualism, and (iii) evaluate ontology editors for their ability to manage a multilingual ontology. Nine different principal approaches for modelling multilinguality in ontologies were identified, which fall into either of the following approaches: using multilingual labels, linguistic models, or a mapping-based approach. They are compared on design by means of an ad hoc visualisation mode of modelling multilingual information for ontologies, shortcomings, and what issues they aim to solve. For the ontologies, we extracted production-level and accessible ontologies from BioPortal and the LOV repositories, which had, at best, 6.77% and 15.74% multilingual ontologies, respectively, where most of them have only partial translations and they all use a labels-based approach only. Based on a set of nine tool requirements for managing multilingual ontologies, the assessment of seven relevant ontology editors showed that there are significant gaps in tooling support, with VocBench 3 nearest of meeting them all. This stock-taking may function as a new baseline and motivate new research directions for multilingual ontologies.

Besides the benefits for the community of people with cognitive disabilities, it can also become a useful educational tool in the foreign language classroom, where students with a limited knowledge of a target language can rely on it to adapt complex literary works.

Learning to speak a new language brings health benefits and in addition we could be predisposed to speak more than one language, new research suggests. In this spirit multilingualism has shown to have social, psychological and lifestyle advantages as well as direct health benefits like faster recovery from a stroke or delaying dementía. Something that could mean that to reach your brain's full potential and to "empower" it, multilingualism is a great practice. This is at the same time a practice that brings a lot of "headache" for artificial intelligence. As Will Knight, senior editor for AI and robotics at MIT Technology Review writes: "There's an obvious problem with applying deep learning to language. It's that words are arbitrary symbols, and as such they are fundamentally different from imagery."

Recently a Quebec arts foundation required the Francophone rap group Dead Obies to give back an 18,000 grant they'd been awarded to record their newest album. A word count determined that the group had stirred too much English into their distinctive multilingual lyrics, falling short of the rule that 70 percent of the content be in French. Dough to get I got more shows to rip Dead-O on the road again, c'est mon tour de get Sous le spotlight, viens donc voir le dopest set We just gettin' started et pis t'es captivated Looking at me now, thinking: «How'd he made it?» Dead Obies is used to catching flak for their language mixing. In 2014, they were excoriated by several French-language journalists for their mongrel lyrics. Christian Rioux, writing for Le Devoir, suggested that such language practices were "suicidal" and would likely result in the formation of a "mediocre creole" incomprehensible to speakers of proper French or English.