English is a West Germanic language of the Indo-European language family that emerged in early medieval England and has since become a global lingua franca. The language is named after the Angles, one of the Germanic peoples who migrated to Britain after the end of Roman rule. English is the most spoken language in the world, primarily due to the global influence of the British Empire and the rise of the United States. It is the most widely learned second language in the world, with more second‑language speakers than native speakers. However, English is only the third‑most spoken native language, after Mandarin Chinese and Spanish.
1,000 most common Dutch words – Understanding Dutch
The 1000 most common words in the Dutch language, with English translations
言語データベースとソフトウェア - 言語データベースとソフトウェア
How linguistics learned to stop worrying and love the language models
Language models (LMs) can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.
Leshem (Legend) Choshen 🤖🤗 on Twitter / X
We know models fail to generalize across languages.But Adam found they fail to generalize when you just call half your English pretraining by another name, like "En2"😵💫 https://t.co/DfWJRP51Fz— Leshem (Legend) Choshen 🤖🤗 (@LChoshen) April 30, 2026
Welcome | Yale Grammatical Diversity Project: English in North America
This Project explores syntactic diversity found in varieties of English spoken in North America. By documenting the subtle, but systematic, differences in the syntax of English varieties, it provides a crucial source of data for the development of theories of human linguistic knowledge.
Python overtakes French as the most popular ‘language’ taught in primary schools
A year ago today the Computing curriculum was introduced by the government. Python now overtakes French as the most popular 'language' taught in primay schools.
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The Rise and Fall of Visual Basic
After 28 years, are we are finally seeing the beginning of the end for one of the world’s most popular languages?
Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs
LLMs have been showing limitations when it comes to cultural coverage and competence, and in some cases show regional biases such as amplifying Western and Anglocentric viewpoints. While there have been works analysing the cultural capabilities of LLMs, there has not been specific work on highlighting LLM regional preferences when it comes to cultural-related questions. In this work, we propose a new dataset based on a comprehensive taxonomy of Culture-Related Open Questions (CROQ). The results show that, contrary to previous cultural bias work, LLMs show a clear tendency towards countries such as Japan. Moveover, our results show that when prompting in languages such as English or other high-resource ones, LLMs tend to provide more diverse outputs and show less inclinations towards answering questions highlighting countries for which the input language is an official language. Finally, we also investigate at which point of LLM training this cultural bias emerges, with our results suggesting that the first clear signs appear after supervised fine-tuning, and not during pre-training.
Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs
LLMs have been showing limitations when it comes to cultural coverage and competence, and in some cases show regional biases such as amplifying Western and Anglocentric viewpoints. While there have been works analysing the cultural capabilities of LLMs, there has not been specific work on highlighting LLM regional preferences when it comes to cultural-related questions. In this work, we propose a new dataset based on a comprehensive taxonomy of Culture-Related Open Questions (CROQ). The results show that, contrary to previous cultural bias work, LLMs show a clear tendency towards countries such as Japan. Moveover, our results show that when prompting in languages such as English or other high-resource ones, LLMs tend to provide more diverse outputs and show less inclinations towards answering questions highlighting countries for which the input language is an official language. Finally, we also investigate at which point of LLM training this cultural bias emerges, with our results suggesting that the first clear signs appear after supervised fine-tuning, and not during pre-training.
Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via...
Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptation is generally...
Best Language School in Massachusetts | Rola Languages
Rola Languages is one of the best language institutes in Boston. Offering small group and private intro-advanced classes in Spanish, French, Portuguese, Italian, Mandarin, and English in-person and online.
The future of language
These charts will help you choose the language you should focus on today.
Why your family’s secret language is good for kids
‘That whoa-whoa is chasing the hodgie!’ Your household’s familect can strengthen bonds and develop language skills.
How Much Money Learning a Foreign Language Is Worth
Besides the personal satisfaction of learning a new language, becoming fluent in a language could boost your lifetime earnings. The Economist takes a
When I worked in software dev, I was taught to check three non-English languages when testing text strings: German to test the largest possible version of a string, Japanese or Chinese to test the shortest possible version, and Thai to check the TALLEST possible version.
