Thanh Tung Vu

Data/Research Scientist | Machine Learning and Optimization Specialist | Research-Led, Business-Focused AI Delivery

8 minute read

Large Language Models (LLMs) like GPT-4 and ChatGPT are reshaping how businesses operate — streamlining content creation, automating knowledge work, improving decision-making, and powering a new wave of AI-driven products. But what gives these models their capabilities in the first place?

The answer lies in pre-training — a massive, foundational learning process where LLMs absorb language, reasoning patterns, and world knowledge by processing vast amounts of text. While most companies don’t pre-train models themselves, understanding how pre-training works is essential for anyone who builds with LLMs or integrates them into products.

In this post, we’ll unpack the full story behind LLM pre-training:

  • What LLM pre-training does and why it matters for model capabilities
  • How the full pre-training pipeline works — from tokenization to training loops
  • Why understanding it is critical for product, engineering, and data teams

Whether you’re selecting the right foundation model, fine-tuning it for your domain, or evaluating model risks and limitations, this guide will help you understand the engine under the hood.

LLM Pre-training

✅ What Is Pre-training?

Pre-training is the process of teaching a large language model general language skills by exposing it to massive amounts of text data. The model learns to predict the next token (word or subword) given previous tokens, a task known as causal language modeling.

For example, given the sentence:

“The capital of France is ___”

The model learns to predict the most likely next word — “Paris” — based on the context.

This single objective turns out to be incredibly powerful: by learning to predict the next token, the model acquires knowledge about syntax, semantics, world facts, reasoning, and more.

💡 Why Is Pre-training Needed?

  1. To Build General Language Understanding

    • Pre-training exposes the model to large-scale text so it can learn syntax, semantics, and real-world knowledge. This equips the model with a broad understanding of language, facts, and logic, much like how humans learn from reading.

  2. To Reduce Dependence on Task-Specific Supervision

    • It uses self-supervised learning, meaning it doesn’t require manually labeled data. Models can leverage massive unlabeled corpora. This makes it possible to train powerful general-purpose models without needing labeled data at scale.

  3. To Enable Knowledge Transfer

    • Once pre-trained, the model can be fine-tuned or adapted to many downstream tasks: summarization, coding, translation, and more, reducing the need to train models from scratch for each one.

  4. To Improve Sample Efficiency and Performance

    • Pre-trained models often achieve strong performance with less labeled data and fine-tuning. This leads to better generalization, especially in low-resource or few-shot settings.

⚙️ How LLM Pre-training Works

1. Input Processing

  • The raw input text is first tokenized into a sequence of discrete tokens: \(x_1, x_2, \dots, x_T\).
  • Each token \(x_t\) is mapped to a token embedding vector using a learnable embedding matrix \(W^E \in \mathbb{R}^{V \times d}\), where \(V\) is the vocabulary size and \(d\) is the model’s hidden dimension.
  • \(W^E[x_t]\) denotes the lookup operation, retrieving the row vector in \(W^E\) corresponding to the token ID \(x_t\). This produces an embedding vector of dimension \(\mathbb{R}^d\).

  • Simultaneously, a positional embedding vector is added to each token to encode its position in the sequence. This is provided by another learnable matrix \(W^P \in \mathbb{R}^{T \times d}\), where \(T\) is the maximum sequence length.

  • The result is a sequence of input vectors:

    \[h_0^{(t)} = W^E[x_t] + W^P[t] \in \mathbb{R}^d\]

    for each position \(t = 1, 2, \dots, T\). This sequence \(h_0^{(1)}, h_0^{(2)}, \dots, h_0^{(T)}\) forms the initial input to the first Transformer layer.

  • Both \(W^E\) and \(W^P\) are learnable parameters and are part of the model’s overall parameter set \(\theta\). These are updated during training via backpropagation to improve the model’s language understanding capabilities.

  • These enriched input representations are then passed through the stacked Transformer layers, where more complex contextual features are learned at deeper levels of the network.

2. Transformer Layers

  • These layers take the embedded input sequence and transform it through a stack of self-attention and feedforward blocks, producing a contextualized representation for each token in the sequence.

  • The output for each token position \(t\) after the final Transformer layer is a vector \(h_t\), which captures its meaning in context.

  • Each Transformer layer consists of two main sub-blocks:

    1. Self-attention block
    2. Feedforward block

  • Both sub-blocks are wrapped with:

    • Residual connection (Add): the input is added to the output of the sub-block.
    • Layer Normalization (LayerNorm): applied either before or after the sub-block (depending on architecture).

  • Each LayerNorm includes learnable parameters:

    • Gain: \(\gamma \in \mathbb{R}^{d}\)
    • Bias: \(\beta \in \mathbb{R}^{d}\)

  • Summary of learnable components per layer:

    • Self-attention: \(W^Q, W^K, W^V, W^O\)
    • Feedforward Network (FFN): \(W_1, W_2, b_1, b_2\)
    • LayerNorm Parameters: \(\gamma, \beta\) for each normalization layer

3. Output Projection

  • The final hidden state \(h_t\) is passed to a language modeling head:

    \[\text{logits}_t = h_t \cdot W^{LM}\]
    • \(W^{LM} \in \mathbb{R}^{d \times V}\) is learnable matrix that projects the hidden state to a vector of vocabulary-sized logits. Each element in the resulting vector corresponds to a score (logit) for one vocabulary token.
    • A softmax turns logits into probabilities over all possible next tokens:
    \[P_\theta(x_{t+1} = x_{t+1}^* \mid x_{\leq t}) = \frac{\exp(\text{logits}_t[x_{t+1}^*])}{\sum_{j=1}^{V} \exp(\text{logits}_t[j])}\]

    where:

    • \(x_{t+1}^*\) is the actual next token,
    • \(\text{logits}_t[x_{t+1}^*]\) is its unnormalized score,
    • and \(V\) is the vocabulary size.

4. Loss Function

  • The model is trained to minimize the cross-entropy loss between the predicted distribution and the actual next token:

    \[\mathcal{L}(\theta) = -\sum_{t=1}^{T} \log P_\theta(x_{t+1} = x_{t+1}^* \mid x_{\leq t})\]

  • The variable \(\theta\) is the stack of all learnable parameters in the model:

\[\theta = \{W^E, W^P, W^Q, W^K, W^V, W^O, W_1, b_1, W_2, b_2, \gamma, \beta, W^{LM}\}\]

5. Update Parameters

After computing the loss for the current batch of training sequences, the model updates its parameters to improve future predictions.

  • The loss function \(\mathcal{L}(\theta)\) measures how far the model’s predicted token distribution is from the actual next token.
  • Using backpropagation, the model computes the gradients of the loss with respect to each parameter in \(\theta\).
  • These gradients are then used to update the parameters via an optimizer. A common choice is:
    • Stochastic Gradient Descent (SGD): Simple and effective but requires careful learning rate tuning.
    • AdamW: A popular adaptive optimizer that combines momentum and adaptive learning rates, with weight decay for better generalization.

The result is an updated parameter set \(\theta \rightarrow \theta'\) that should reduce the loss on future sequences.

6. Training Loop

This process of forward pass → loss computation → backpropagation → parameter update is repeated across billions of training examples.

  • Each iteration is called a training step, and a full pass over the dataset is an epoch.
  • In practice, models are trained over shuffled batches of token sequences sampled from large corpora like Wikipedia, books, and web data.
  • Training can run for weeks on massive clusters of GPUs or TPUs.

Over time, the model accumulates patterns, relationships, and facts from the training data, effectively learning a statistical map of language. This pre-trained knowledge serves as a foundation for downstream tasks via prompting or fine-tuning.

🏭 Do Companies Outside Big Tech Pre-train LLMs?

In practice, most companies outside Big Tech (e.g., OpenAI, Google, Meta) do not pre-train LLMs from scratch — and for good reason.

Why Not?

  1. Enormous Cost
    Pre-training a GPT-style model requires thousands of GPUs or TPUs, running for weeks or months. This can cost millions of dollars.

  2. Massive Data Requirements
    You need trillions of tokens of well-curated text — cleaned, deduplicated, and legally safe. This is far from trivial to assemble and maintain.

  3. Deep Infrastructure & Expertise
    Successful pre-training demands distributed systems engineering, scalable storage, monitoring, optimization tuning, and error resilience at scale.

So What Do Most Companies Do?

  • Use pre-trained models (e.g., GPT-4, Claude, Mistral) via APIs
  • Fine-tune open-source models (e.g., LLaMA, Falcon) on their domain data
  • Apply parameter-efficient fine-tuning (e.g., LoRA, Adapters)
  • Train smaller domain-specific models where needed (e.g., in biotech, finance, law)

Exceptions?

Some non–Big Tech organizations do pre-train LLMs, usually for specific domains:

  • BloombergGPT (finance)
  • BioGPT, PubMedGPT (biomedical)
  • Mistral, Falcon, Salesforce CodeGen (from well-funded labs/startups)

🎓 Why Learn LLM Pre-training If You’re Not Doing It?

Even if you’re not pre-training a model from scratch, understanding LLM pre-training is essential if you’re working with LLMs.

1. Understand What You’re Working With

Pre-training shapes the model’s knowledge, biases, and limitations. Knowing how it’s trained helps you:

  • Interpret behavior
  • Avoid misuse
  • Prompt more effectively

2. Improve Fine-tuning & Adaptation

All downstream use cases (fine-tuning, RAG, prompting) start from a pre-trained base. Understanding that base helps you:

  • Choose the right model
  • Design better adaptations
  • Avoid redundant training

3. Make Better Model Choices

Should you use a closed API or an open-source model? One trained on code? On medical papers?

  • Understanding pre-training data and objectives helps you select the right model for the job.

4. Plan for Privacy, IP, and Compliance

If your application handles sensitive data:

  • You need to know what kind of data the model might have been trained on
  • And whether that raises concerns about memorization, leakage, or IP risk

5. Be Future-ready

As tooling becomes cheaper and more accessible:

  • You may eventually pre-train a small domain-specific model
  • Or contribute to open-source efforts

🎯 Final Thoughts

While only a handful of organizations have the resources to pre-train LLMs from scratch, understanding how pre-training works is essential for anyone building with them.

Pre-training is what gives LLMs their broad language competence, world knowledge, and reasoning ability. It defines the model’s strengths and limitations, which shape how you prompt, fine-tune, evaluate, and deploy it.

Even if you’re leveraging APIs or adapting open-source models, you’re standing on the shoulders of this massive training process. The better you understand it, the more effectively — and responsibly — you can work with LLMs.

So next time an LLM answers your question or writes your code, remember: it all began with the quiet, token-by-token grind of pre-training.

🧠 Up Next: Want to dive into fine-tuning and how LLMs adapt to specific tasks? Stay tuned for the next post!

For further inquiries or collaboration, feel free to contact me at my email.

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