The rise of Large Language Models (LLMs) like GPT, Gemini, and LLaMA has revolutionised natural language processing by enabling models to perform diverse tasks with minimal task-specific supervision. Central to their effectiveness is the multi-stage training process these models undergo, training phases such as pretraining, fine-tuning, and increasingly, instruction tuning for certain use cases where a user instruction needs to be followed.
Understanding these stages is essential for aspiring ML engineers and organisations leveraging foundation models. Here’s a short breakdown of what each training phase entails, why it matters, and how they differ.
Objective: Equip the model with a general understanding of language, grammar, reasoning patterns, and world knowledge.
Approach:
Pretraining is carried out on vast amounts of unlabelled text using self-supervised learning. In the case of GPT-style models (decoder-only), this means causal language modelling — predicting the next token given the previous ones.
Data Sources:
Outcome:
The model becomes a foundation model — a general-purpose LLM capable of understanding and generating natural language.
Example: GPT-3 is pretrained on 300+ billion tokens from a wide range of internet sources, without any explicit task-specific labels.
Objective: Adapt the pretrained model to perform a specific downstream task such as sentiment analysis, named entity recognition, summarisation, or domain-specific QA.
Approach:
Fine-tuning uses supervised learning on labelled datasets, where the model is trained to map from inputs to expected outputs.
Examples of Tasks:
Why it matters:
Fine-tuning enables customisation and improves performance in specialised contexts, making the model practical for real-world applications.
Example: Fine-tuning GPT on financial documents to improve reasoning over balance sheets and contracts.
Objective: Teach the model to follow instructions and perform multiple tasks based on natural language prompts.
Approach:
Instruction tuning is a form of supervised fine-tuning, but instead of training on isolated tasks, the model is trained on a diverse set of instruction–response pairs.
Instruction tuning is typically done after pretraining (and sometimes after fine-tuning), enabling models to generalise to unseen instructions.
Why it matters:
This step dramatically improves usability by aligning the model’s behaviour with human expectations, making it more helpful, reliable, and controllable.
Example: InstructGPT outperforms GPT-3 on many NLP tasks despite being smaller, thanks to instruction tuning on prompt–response examples.
A post-training technique that aligns the LLM to human preferences. Initially collect human preferences and use that data to align LLMs
The journey from raw text to task-aware intelligence involves strategically combining pretraining, fine-tuning, and instruction tuning. Each phase builds upon the previous one, starting from foundational knowledge, to specialised skills, and ultimately to usable, aligned behaviour.
As models continue to scale and evolve, mastering these training paradigms is crucial for deploying LLMs safely and effectively in real-world systems.