LLMs like Chat GPT don't actually understand text the way a person does. They don't have concepts or any life experience. When you type something, the model turns your text into a bunch of numbers (called a "vector"). Every token (like a word or part of a word) is basically a point with coordinates in a massive, high-dimensional space. The distance between these points shows how related their meanings are

For example, the vectors for "king" and "queen" will end up being really close together, while the vectors for "king" and "table" will be way far apart. Then, the transformer part kicks in with its self-attention mechanism. This is a fancy way of saying it analyzes how all the words in your text relate to each other and figures out how much "attention" to pay to each one. This is how the model gets the context. It's how it knows that the "bank" in "river bank" is totally different from the "bank" in "open a bank account" Based on all those relationships, it then predicts the next token. But it's not just guessing -it's making a highly probable prediction based on all the context it just looked at

To put it simply: the model isn't "aware" of what anything means. It's just incredibly good at modeling how meaning is expressed in language

ChatGPT doesn't truly "understand" language the way humans do, but it models language in a highly advanced way by training & learning from a vast amounts of data. The key tech behind ChatGPT's ability to grasp meaning is their transformer architecture (self-attention mechanism). It allows the model to weigh and focus on different words in a sentence based on their importance or context. In simpler terms, it looks at how each word relates to every other word in the sentence and beyond. This allows it to understand context and nuances, even within long or abstract sentences.

Furthermore, ChatGPT (and other LLMs) is trained on a massive corpus of text, books, articles, websites, etc. From that training, the model learns patterns in how words, phrases, and sentences related to one another. It doesn't explicitly understand what a "dog" or "love" means in the human sense, but it understands it patterns about how they are expressed and used in language.

Without going into too much details, it also uses other techniques like Probabilistic Modeling and Semantic Representations to essentially be able to provide you with what it does currently.

If you wish to dive deeper and do some research, I'd recommend checking out the following:

1. Transformer Architecture 2. Self-Attention Mechanism 3. Pre-trained language models 4. Embeddings and Semantic Space 5. Attention is All You Need - which is a paper published by Vaswani et al., very interesting publication that is a key for understanding the self-attention mechanism and how it powers modern NLP models like GPT. 6. Contextual Language Models

I think those 6 would cover up all your questions and doubts

It sounds like you get that LLMs are just "next word" predictors. So the piece you may be missing is simply that behind the scenes, your prompt gets "rephrased" in a way that makes generating the response a simple matter of predicting the next word repeatedly. So it's not necessary for the LLM to "understand" your prompt the way you're imagining, this is just an illusion caused by extremely good next-word prediction.

In my simple mind "Who is the queen of Spain?" becomes "The queen of Spain is ...".

Good intro here that talks about how embeddings work! https://www.youtube.com/watch?v=wjZofJX0v4M

The robot is copying how humans have previously responded to queries similar to yours, and semantically rephrasing to align with OpenAI/Microsoft's desired brand image.