Millan Kaul

2 minute read

Written by - Millan Kaul

What is Pre-training in LLMs?

Pre-training equips LLMs with broad language knowledge from vast datasets before task-specific tuning.

WHY?

For Developers and SDETs

  • Pre-training is how LLMs learn patterns, grammar, and world knowledge from internet-scale data, giving them the “smarts” to handle diverse test prompts without task-specific training.
  • Understanding pre-training helps you know what the model “knows” by default (syntax, facts, code patterns) vs what it needs to learn later (your domain rules, policies).

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WHAT?

  • Pre-training is the initial training phase where a transformer model learns general language understanding by predicting parts of massive unlabeled text datasets (books, web, code).
  • Next-token prediction (autoregressive, GPT-style): model sees text so far and predicts the next word/token repeatedly.
  • Masked language modeling (bidirectional, BERT-style): randomly hide 15% of tokens and predict them using full context from both sides.

Take these concrete examples:

  • Next-token: “The cat sat on the” → predict “mat”.
  • Masked: “The [MASK] sat on the mat” → predict “cat”.
  • Trained on trillions of tokens from diverse sources like Common Crawl, books, Wikipedia, code repos.

WHEN AND WHERE?

When pre-training knowledge is key

  • When using zero/few-shot prompting: the model’s pre-training knowledge is what enables it to follow instructions or reason without examples.
  • When debugging unexpected knowledge: model recalls facts, code patterns, or behaviors it “learned” during pre-training.

When you can keep it high-level

  • Daily prompt engineering doesn’t require pre-training details; just know it’s the “general smarts” before specialization.

Think about where pre-training impacts your work.

  • Model selection docs: “Pre-trained on X trillion tokens, cutoff YYYY-MM” tells you the scope of built-in knowledge.
  • Zero-shot capabilities: ability to summarize, translate, code-review without fine-tuning comes from pre-training.

Concrete examples:

  • GPT-4 pre-trained to April 2023 knows events up to then but not later (hence hallucinations on fresh data).
  • Code models pre-trained on GitHub repos generate syntax and patterns without task training.
  • Multilingual models handle 50+ languages because pre-training included diverse web text.

HOW?

1. Conceptual steps

  1. Massive data preparation
    • Clean/filter trillions of tokens from web crawls, books, code (remove duplicates, toxic content).
  2. Self-supervised objectives
    • Next-token: shift input/output by 1, predict forward (decoder-only).
    • Masked: hide random tokens, predict using bidirectional context (encoder).
  3. Train transformer at scale
    • Stack 100s of layers, train on 1000s of GPUs for weeks/months, optimizing next-token or masked loss.
  4. Checkpoint and evaluate
    • Save weights when perplexity plateaus; test on benchmarks like GLUE, MMLU.

2. Examples

  • Next-token training: “The quick brown fox jumps over the lazy” → predict “dog” (learns grammar, facts).
  • Masked training: “The [MASK] [MASK] over the lazy dog” → predict “quick brown fox jumps” (learns bidirectional context).
  • Outcome: model learns syntax (“noun verb”), semantics (“animal action”), facts (“Paris=France”).

3. Testing mindset

  • Knowledge probes: ask about pre-training-era facts; gaps show cutoff or weak coverage.
  • Pattern tests: does it complete common code snippets or sentences correctly?

For Leaders

  • Pre-training is the expensive foundation that makes LLMs versatile; it explains why even “zero-shot” prompts work reasonably well out-of-the-box.
  • Leaders care because pre-training costs drive model pricing, and its quality sets the ceiling for what fine-tuning or prompting can achieve.
  • When selecting base models—pre-training quality affects generalization, safety baselines, and how much fine-tuning you’ll need.
  • Focus on outcomes (context length, knowledge cutoff) rather than training recipes.
  • In cost models—pre-training is done once by providers; you pay via API costs scaled by model size.
  • Benchmark zero-shot performance on your domain docs to gauge if pre-training suffices or fine-tuning is needed.
  • Pre-training uses self-supervision (no human labels needed), making it scalable but compute-intensive (weeks/months on thousands of GPUs).

Reference

  • “What is LLM training?” IBM
  • “LLM Pre-Training and Custom LLMs” Databricks

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