Hacking Scale by Better Stack
LinkedIn uses large language models (LLMs) for many features, just like other tech companies. Features like job matching and providing users with relevant content.
This involves giving the model large unstructured data like books, articles, and websites. The data is stored in text, JSON, or binary format.
Then, words are tokenized into a numerical representation. So a sentence like "I love programming" could be represented as [1, 23, 456]
These tokens are converted to embeddings, which capture words or phrases in a dense numerical format. This helps the model understand their meaning and relationship to other words.
Here, the model learns patterns by predicting the next word in a sentence or guessing missing words. This is all done automatically (self-supervised).
The model has the answers, so it tries to predict the answer and then checks it against the correct sentence. If it's correct or incorrect, it adjusts its weights to reduce the chances of a wrong prediction (backpropagation ).
