Large language models are trained by processing vast collections of tokenized text and repeatedly adjusting billions of numerical settings to improve next-token predictions. Most then undergo post-training, in which human demonstrations and rankings help them follow instructions and avoid some unwanted behavior. A large language model, or LLM, is a system that generates text through learned predictions. A token is a unit of text the model processes, while a parameter is an adjustable number that shapes those predictions.
Table of Contents
- How text becomes training data
- What happens during pretraining
- Why scale alone does not determine quality
- What post-training changes
- Why a trained model can still be wrong
How text becomes training data
Developers first collect and curate large bodies of text. OpenAI says GPT-4's pretraining corpus included public internet data and licensed data. Raw text requires extensive cleaning.
Meta reports that Llama 3's public-source corpus underwent heuristic filtering, filtering for sexually explicit content, semantic deduplication and quality classification in its description of the model's training. Developers also try to remove repeated documents and material overlapping with evaluations. Without that work, duplicate or poor-quality text can distort learning, while benchmark overlap can make performance tests less meaningful.
What happens during pretraining
Modern LLMs generally use the Transformer architecture. Transformers rely on attention mechanisms that help the model process relationships within a sequence while making training highly parallelizable, according to the researchers behind "Attention Is All You Need". During pretraining, the model receives a sequence of tokens and predicts the next one.
The training system compares that prediction with the actual token, calculates the error and adjusts the model's parameters through gradient-based optimization. This cycle repeats across vast numbers of sequences. The result is a model whose parameters encode statistical patterns learned from the training material, including patterns that support instruction following, reasoning and text generation.
Why scale alone does not determine quality
Training large models requires developers to divide work across computing hardware. they may split the training data, the model itself and different processing stages among many graphics processing units. More parameters do not automatically produce the strongest model for a fixed computing budget.
📨 Get Free News Stories Alerts
Free · No spam · Unsubscribe anytime
Google DeepMind reported that its 70-billion-parameter Chinchilla model, trained on 1.3 trillion tokens, outperformed the 280-billion-parameter Gopher model using the same training compute in its analysis of compute-optimal training. The practical issue is balance. Developers must allocate limited computing power among model size, the amount of training text and the number of optimization steps.
What post-training changes
Pretraining teaches broad capabilities, but it does not necessarily make a model respond as users expect. Developers can collect examples of preferred answers, ask people to rank alternative responses and train a reward model from those preferences.
They can then optimize the language model using reinforcement learning from human feedback, commonly called RLHF. OpenAI reports that GPT-4's core capabilities came mainly from pretraining, while RLHF helped steer its behavior toward user intent and guardrails.
Why a trained model can still be wrong
Training rewards plausible next-token predictions, not independent verification of every statement. A fluent response can therefore contain false claims or internal contradictions.
NIST warns that this risk is especially important in long, open-ended answers, expert fields and consequential decisions in its Generative AI risk-management profile. Readers should check important claims against underlying primary sources before acting on medical, legal, financial or other high-stakes information.
You Might Also Like
- Judge Calls Florida Property Tax Ballot Language Defective, Giving AG 10 Days to Rewrite
- How Credit Scores Are Calculated
- Anonymous Sources: When They Matter and When They Are a Red Flag