How are large language models trained and evaluated?

Large language models are like super-smart students who learn by reading and practicing a lot.

Imagine you're teaching your friend to read. You give them books, they read sentences, and then you ask them questions about what they just read. The more they practice, the better they get at understanding stories, answering questions, and even writing their own sentences. That's kind of how large language models are trained, but with tons of books and questions.

Training is like giving a student a huge library to study from. A model reads many different texts, like books, articles, or conversations, so it learns patterns in how words are used together. The more examples it sees, the better it gets at predicting what word comes next, or even completing a sentence.

Once they've learned a lot, evaluation is like giving them a quiz. You give them new sentences and ask them to fill in blanks, answer questions, or write their own stories. If they do well on the quiz, it means they really understood what they studied, just like you'd know your friend is a good reader if they ace the test.

The more practice they get, the better they become at understanding and creating language, just like you!