Role of Context in LLMs

Let's dive a bit deeper into the world of word vectors and explore how context comes into play.

Imagine you're trying to understand the word "apple." Without context, it could be a fruit or a tech company. But what if I say, "I ate an apple"? Now it's clear, right? Context helps us make sense of words, and it's no different for large language models.

Technical Explanation Made Simple

In general, large language models like GPT-4 or Llama use various techniques to understand the context surrounding each word. For instance, GPT-4 leverages a popular and efficient technique called the "attention mechanism," which helps the model focus on different parts of the text to understand it better. However, older models might use other strategies like Recurrent Neural Networks (RNNs) or Long Short-Term Memory Networks (LSTMs) to capture context differently.

Whether it's attention mechanisms or RNNs, the goal is the same: to give the model a better understanding of how words relate to each other. This understanding is crucial for tasks like language translation, text summarisation, and question answering.

Now you know how Context Matters

Context is not just a technical requirement but a functional necessity. By understanding the context, these models can perform tasks ranging from simple ones like spelling correction to complex ones like reading comprehension.

So, the next time you see a language model perform a task incredibly well, remember that it's not just about the individual words but also the context in which they are used.