the number of t. If the number

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Large language models largely ignore most of the world's existing languages. For example, the following languages ​​with large populations contribute less than % of Internet text content, so it is difficult to collect enough data to train a large language model specifically for this language. Hindi. 100 million users. Arabic. 100 million users. Bengali. 100 million users. Urdu. 100 million users The difference between language users and available text data leads to an imbalance in language diversity. The source of this problem is more about a country's development and investment strength, which we will elaborate on in the next blog post.



This is also a fundamental challenge for large iran telephone number language models that aim to support a wider range of languages. If a language has only a small amount of text on the Internet, there is no large language model suitable for this language. If a language has a large amount of text on the Internet, it also requires its representative country to increase investment to develop a large language model with its own language characteristics. So I classified the world's languages ​​according to the support of htT-. Support for high-resource and low-resource languages ​​English is the most effective "programming language" for large language models.



Large language models have an input and output limit expressed in few, then what can be done is very limited. This is a bit like the early personal computers that had only 10000 memory and could not run "big programs". Today, some smartphones have 100000 times the memory of the past.As for how many English words or Chinese characters a t is, we will explain it later. The length of t for language models such as -.-t and T--t has been growing.