A language model is a type of artificial intelligence system trained to understand and process human language. Think of it as a system that has learned patterns in language by analyzing vast amounts of text data - everything from books and articles to websites and documents.
The key thing about language models is that they learn to predict and understand words in context. For example, if you see the phrase "I'm going to drink a cup of ___", a language model would predict that "coffee" or "tea" are likely words to fill that blank because it has learned that these words commonly appear in this context. But it's much more sophisticated than simple word prediction or n-gram models - modern language models understand complex relationships between words, can grasp meaning across long passages, and can even understand nuances like sarcasm or technical jargon.
These models work by converting words into numerical vectors (embeddings) that capture their meaning and relationships to other words. The larger and more sophisticated the model, the more nuanced its understanding can be. Large language models like GPT-4 can engage in complex conversations and generate text, while smaller specialized models like MiniLM are optimized for specific tasks like comparing the similarity between pieces of text.
The real power of language models comes from their ability to turn human language into a format that computers can process mathematically while preserving the meaning and context that makes language useful for communication.
