How do LLMs generate text?

17 Tem 01:52

Good question actually. Let's try to simplify this process. LLMs, especially those using the Transformer architecture (like most modern models, including me), generally follow this approach: When it receives a question, it first converts each word in the sentence into numerical vectors (embeddings) through tokenization. These vectors encode not just the meaning of the words but also their position in the sentence and context. Then, the attention mechanism kicks in: the model calculates how words are related to each other and which words are more important. For example, it might weigh the relationship between "on Mars" and "is there life" more heavily. Thanks to this attention mechanism, the model can prioritize the word "Mars" over "life" if it deems it more relevant. Next, it uses the most relevant data within the context window to generate an answer. The context window size is limited (ranging from 0 to 128K, e.g., Llama 3 has 128K), so it struggles to remember very long pieces of information. Different models have different window sizes, but if the window is large enough (and the question is simple), it can directly copy the answer from patterns in its memory. For instance, to the question "Is there life on Mars?", it might respond with "While scientists have found traces of water in the past, there is no definitive evidence," because this information is a high-frequency pattern in its training data. So, in the end, it’s essentially using a kind of "compressed and statistical memory."

17 Tem 02:09

LLMs generating text is like a person memorizing and reciting a long text, but much more automated. Say we have millions of documents containing answers to the question "Is there life on Mars?"—an LLM "reads" these documents, learning how words, sentences, and even paragraphs connect to each other. This works through something called the "attention mechanism," where the LLM calculates which words are semantically more connected to each other, much like how you focus on key parts while reading a text. The patterns it learns are essentially a form of "behavioral mimicry": the LLM generates the most statistically likely sequence of words for a given question based solely on patterns in the data. You can think of the context window like a movie script. The question you feed an LLM and the preceding sentences form the current scene. For example, when answering "Is there life on Mars?", the LLM doesn’t just look at the last question—it scans the entire preceding text (e.g., "Research is being conducted on life in other planets in the solar system. Mars...") all at once. This "window" limits the context the LLM needs to focus on while filtering out irrelevant details—like an editor skimming only the relevant sections of an article. The most fascinating part? Despite not "understanding" in a human sense, the LLM produces human-like responses: the word sequences it generates make it seem as though it truly grasps the topic.

17 Tem 02:47

The way an LLM answers the question "Is there life on Mars?" is kind of like solving a puzzle made up of fragments from massive books—like "Traces of life found on Mars"—by figuring out the most likely word chain. With its attention mechanism, it determines which pieces fit together best and then spits out the most plausible answer to your question.

17 Tem 04:36

LLMs generate text by using statistical patterns they've learned from a vast ocean of pre-consumed data, constructing the most appropriate word chain for your query through a massive computational system. When you ask, "Is there life on Mars?" the model first scans tokens (word fragments) like "Mars," "life," and "is there" within its context window and tries to predict the most likely sequence of tokens. The key here is the attention mechanism: as each word in the sentence "pays attention" to others, it calculates how relevant these words are to the answer and ultimately selects the most fitting word for the response, such as, "Due to lack of information, there is no clear answer yet." From my observations, while this system works quite consistently for simple questions, as the topic delves into scientific details, the word combinations the model fabricates also increase. For instance, when discussing topics like "the potential for colonization on Mars," some models generate sentences based on actual research, while others have been known to absurdly fabricate things like "monkey astronauts sent to Mars." That’s why, even if you get an answer, it’s always worth checking secondary sources.