{"id":2273,"date":"2026-08-14T06:37:13","date_gmt":"2026-08-14T06:37:13","guid":{"rendered":"https:\/\/www.vaultinsider.top\/?p=2273"},"modified":"2026-08-14T06:37:13","modified_gmt":"2026-08-14T06:37:13","slug":"the-worlds-best-ai-models-operate-in-english-other-languages-even-major-ones-like-cantonese-risk-falling-further-behind","status":"publish","type":"post","link":"https:\/\/www.vaultinsider.top\/?p=2273","title":{"rendered":"The world\u2019s best AI models operate in English. Other languages\u2014even major ones like Cantonese\u2014risk falling further behind"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/fortune.com\/img-assets\/wp-content\/uploads\/2025\/07\/GettyImages-1764173087-e1752565276225.jpg?w=2048\" \/><\/p>\n<p>How do you translate \u201cdim sum\u201d? Many English speakers would find the question strange, knowing the term refers to the large array of small dishes that accompanies a Cantonese-style brunch\u2014and so doesn\u2019t need translation.\u00a0<\/p>\n<div>\n<p class=\"wp-block-paragraph\">But words like \u201cdim sum\u201d are a challenge for developers like Jacky Chan, who launched a Cantonese large language model last year through his startup Votee. It might be obvious to a human translator what words are loanwords and which need direct translation. Yet it\u2019s less intuitive for machines.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">\u201cIt\u2019s not natural enough,\u201d Chan says. \u201cWhen you see it, you know it\u2019s not something a human writes.\u201d<\/p>\n<p class=\"wp-block-paragraph\">Translation troubles are part of a growing list of issues<strong> <\/strong>when today\u2019s AI models, strongest in English and other major languages, try to work in an array of smaller tongues still spoken by tens of millions of people.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">When AI \u201cmodels encounter a word they don\u2019t know or that doesn\u2019t exist in another culture, they will simply make up a translation,\u201d explains Aliya Bhatia, a senior policy analyst at the Center of Democracy &amp; Technology, where she researches issues related to multilingual AI. \u201cAs a result, many machine-created datasets could feature mistranslations, words that no native speaker actually uses in a specific language.\u201d\u00a0<\/p>\n<p class=\"wp-block-paragraph\">LLMs need data, and lots of it. Text from books, articles and websites is broken down into smaller word sequences to form a model\u2019s training dataset. From this, LLMs learn how to predict the next word in a sequence, eventually generating text.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">AI can now generate text remarkably well\u2014at least, it can in English. In other languages, performance lags significantly.\u00a0Roughly half of all web content is in English, meaning there\u2019s no shortage of digital resources for LLMs to learn from. Many other languages do not enjoy this same abundance.\u00a0<\/p>\n<h2 class=\"wp-block-heading\">Low-resource languages<\/h2>\n<p class=\"wp-block-paragraph\">So-called low resource languages are those with limited online data. Endangered languages, no longer being passed down to younger generations, clearly fall into this category. But widely spoken languages like Cantonese, Vietnamese and Bahasa Indonesia are also considered low-resource.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">One reason could be limited internet access, which would prevent the creation of digital content. Another could be government regulation, which might limit what\u2019s available online. Indonesia, for example, can remove online content without offering a way to appeal decisions. The resulting self-censorship may mean that available data in some regional languages might not represent authentic local culture.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">This resource gap leads to a performance gap: Non-English LLMs are more likely to produce gibberish or inaccurate answers.\u00a0LLMs also struggle with languages that don\u2019t use Latin script, the set of letters used in English, as well as those with tonal features that are hard to represent in writing or code.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Currently, the best-performing models work in English and, to a lesser extent, Mandarin Chinese. That reflects where the world\u2019s biggest tech companies are based. But outside of San Francisco and Hangzhou, a legion of developers, large and small, are trying to make AI work for everyone.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">South Korean internet firm Naver has built an LLM, HyperCLOVA X, which it claims is trained on 6,500 times more Korean data than GPT-4. Naver is also working in markets like Saudi Arabia and Thailand in a bid to expand its business creating \u201csovereign AI,\u201d or AI tailored to a specific country\u2019s needs. \u201cWe focus on what companies and governments that want to use AI would want, and what needs Big Tech can\u2019t fulfill,\u201d CEO Choi Soo-Yeon told <em>Fortune <\/em>last year.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">In Indonesia, telecom operator Indosat and tech startup Goto are collaborating to launch a 70 billion parameter LLM that operates in Bahasa Indonesia as well as five other local languages, including Javanese, Balinese, and Bataknese.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">One hurdle is scale. The most powerful LLMs are massive, made up of billions of word sequences converted into variables known as parameters. OpenAI\u2019s GPT-4 is estimated to have around 1.8 trillion\u202fparameters. DeepSeek\u2019s R1 has 671 billion.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Non-English LLMs seriously struggle to achieve this kind of scale. The Southeast Asian Languages in One Model (SEA-LION) project has trained two models from scratch: One with 3 billion parameters and one with 7 billion, much smaller than leading English and Chinese models.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Chan, from Votee, faces these struggles when dealing with Cantonese, spoken by 85 million people across southern China and Hong Kong. Cantonese uses different grammar for formal writing compared to informal writing and speech. Available digital data is scarce and often low-quality.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Training on digitalized Cantonese texts is like \u201clearning from a library with many books, but they have lots of typos, they are poorly translated, or they\u2019re just plain wrong,\u201d says Chan.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Without a comprehensive dataset, an LLM can\u2019t produce complete results. Data for low-resource language often skews towards formal texts\u2014legal documents, religious texts, or Wikipedia entries\u2014since these are more likely to be digitized. This bias can distort an LLM\u2019s tone, vocabulary and style, and limit its knowledge.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">LLMs have no inherent sense of what is true, and so false or incomplete information will be reproduced as fact. A model trained solely on Vietnamese pop music might struggle to accurately answer questions on historical events, particularly those not related to Vietnam.\u00a0\u00a0<\/p>\n<h2 class=\"wp-block-heading\">Translating English content<\/h2>\n<p class=\"wp-block-paragraph\">Turning English content into the target language is one way to supplement the otherwise-limited training data. As Chan explains, \u201cwe synthesize the data using AI so that we can have more data to do the training.\u201d\u00a0<\/p>\n<p class=\"wp-block-paragraph\">But machine translation carries risk. It can miss linguistic nuance or cultural context. A Georgia Tech study of cultural bias in Arabic LLMs found that AI models trained on Arabic datasets still exhibited Western bias, such as referencing alcoholic beverages in Islamic religious contexts. It turned out that much of the pre-training data for these models came from web-crawled Arabic content that was machine-translated from English, allowing cultural values to sneak through.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">In the long-term, AI-generated content might end up polluting low-resource languages datasets. Chan likens it to \u201ca photocopy of a photocopy,\u201d with each iteration degrading the quality. In 2024, <em>Nature <\/em>warned of \u201cmodel collapse,\u201d where AI-generated text could contaminate the training data for future LLMs, leading to worse performance.\u00a0\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The threat is even greater for low-resource languages. With less genuine content out there, AI-generated content could quickly end up making up a larger share of what\u2019s online in a given language.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Large businesses are starting to realize the opportunities in building a non-English AI. But while these companies are key players in their respective tech sectors, they\u2019re still much smaller than giants like Alibaba, OpenAI, and Microsoft.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Bhatia says more organizations\u2014both for-profit and not-for-profit\u2014need to invest in multilingual AI if this new technology is to be truly global.\u00a0\u00a0<\/p>\n<p class=\"wp-block-paragraph\">\u201cIf LLMs are going to be used to equip people with access to economic opportunities, educational resources, and more, they should work in the languages people use,\u201d she says.\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><em>Fortune is bringing Brainstorm AI back to Asia on July 22-23<\/em> <em>with the latest edition of our Brainstorm AI Singapore conference. Fortune will be convening the smartest people we know\u2014technologists, entrepreneurs, Fortune Global 500 executives, investors, policymakers, and the brilliant minds in between\u2014to explore and interrogate the most pressing questions about AI<\/em>. <em>Register here!<\/em><\/p>\n<\/div>\n<p>#worlds #models #operate #English #languageseven #major #Cantoneserisk #falling<\/p>\n","protected":false},"excerpt":{"rendered":"<p>How do you translate \u201cdim sum\u201d&hellip; <\/p>\n","protected":false},"author":1,"featured_media":2274,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[4415,4421,4416,2343,4422,4417,4418,4420,618,342,4419,2044],"class_list":["post-2273","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-finance-news","tag-asia-migrated","tag-cantoneserisk","tag-editors-picks","tag-english","tag-falling","tag-featured","tag-language","tag-languageseven","tag-major","tag-models","tag-operate","tag-worlds"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The world\u2019s best AI models operate in English. 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