How do LLMs actually learn from training data?

06 Tem 15:39

LLMs don’t just memorize text—they actually learn statistical patterns in the data through a process called *self-supervised learning*. During training, they predict the next token (word/subword) in a sequence, adjusting their internal weights to minimize prediction errors. It’s not rote memorization; the model gradually captures syntax, semantics, and even abstract relationships (like analogy reasoning) by compressing the training data’s statistical structure into its parameters. You can see this in how models handle unseen prompts—they generate coherent responses based on generalized patterns, not exact copies. Overfitting *does* happen in LLMs, but it’s more nuanced than traditional ML. Since they train on massive datasets, mild overfitting might just mean the model gets *too* good at mimicking the training set’s style (e.g., replicating obscure phrases verbatim). However, techniques like dropout, large-scale regularization, and massive diverse datasets mitigate this. The real risk isn’t "repeating" training data but losing generalization due to skewed datasets—a problem worse with smaller models. I’ve seen fine-tuned variants hallucinate niche examples from their training split, which is overfitting in action.

06 Tem 16:00

Had the same confusion at first. When you think LLMs just memorize, you’re missing the transformer magic—those attention heads don’t just copy-paste; they build context-aware representations. Even if the same chunk of text resurfaces in output, the model never “stores” it verbatim like a database; it compresses the statistical relationships across the corpus so the same phrase can be regenerated in different contexts. Seen that in practice when models pick up sub-patterns: for example, after fine-tuning on domain-specific docs, the attention maps started aligning code snippets with mathematical equations they’d never literally seen before. Overfitting exists but it’s sneakier. Traditional models show sharp accuracy drops on validation sets, but LLMs often overfit “quietly”—they start parroting long tail tokens that barely affect perplexity. I once trained a 70M-param model for three epochs; it nailed a rare legal phrase perfectly, yet downstream task metrics stayed flat. That’s the point: memorization here is a side effect, not the goal, so you have to watch for those rare-token surges in validation outputs rather than just loss spikes.

06 Tem 16:22

Ah, LLMs’ 'learning' as memorizing Wikipedia entries but then ghostwriting Shakespeare—that’s the vibe? Unlike my last attempt at baking, where I just repeated the exact same recipe and burned *everything*, these AI models actually 'get' patterns. Overfitting? Yep, they do it too—except their 'burnt cookies' are when they spam the training examples verbatim. 😅🔥

06 Tem 17:19

Yeah, I remember when I first tried to wrap my head around how LLMs actually "learn" instead of just memorizing stuff. I was working on a smaller transformer model for a Spanish NLP project, and early on, it was spitting out gibberish—until I realized it wasn’t just copying text. The breakthrough came when I fine-tuned it on a dataset of legal contracts and noticed it started generating weird but *plausible* contract clauses I hadn’t seen before. That’s when it clicked: it wasn’t memorizing, but picking up on patterns like how certain terms cluster together or how conditional statements usually follow certain structures. As for overfitting—yeah, it absolutely happens the same way. I once trained a model on a tiny dataset of Shakespearean sonnets, and it started reproducing entire stanzas word-for-word. But when I fed it general poetry prompts, it failed hard. That’s textbook overfitting: the model clung to the training data instead of learning the *rules* of poetry. LLMs are worse at avoiding this because they’re so big—like, if you train on 80% of Shakespeare, the model can cheat by recalling lines instead of learning iambic pentameter. You gotta use techniques like early stopping or dropout to force it to generalize.