Training Large Language Models
How LLMs are pretrained, optimised, and adapted using large datasets and distributed compute.
Large-scale training is what enables modern AI systems to reason over text, generate coherent outputs, and support translation and multilingual workflows in production.
What Are Large Language Models
Large language models are neural systems trained to predict and generate token sequences. They learn statistical and semantic patterns from broad corpora.
Pretraining and Training Data
During pretraining, models ingest massive datasets to build general language capabilities. Data diversity, filtering, and deduplication are critical to quality and safety.
Gradient Descent and Optimisation
Training relies on gradient descent, backpropagation, and adaptive optimisers to minimise prediction error across billions of parameters.
GPU and Distributed Training
LLM training uses clusters of GPUs with distributed data/model parallelism. Efficient scheduling and communication are required to scale without instability.
Fine-Tuning and Model Adaptation
After pretraining, fine-tuning adapts models to domain, terminology, and task constraints such as legal translation or customer-support assistants.
Challenges in Training LLMs
Key challenges include compute cost, data governance, model bias, hallucinations, evaluation reliability, and carbon impact. Responsible deployment requires technical and policy controls.
Related Glossary Terms
Related Terms
Terminology Extraction
Automated or manual identification of key terms for project use.
Terminology Management
Organising, updating, and maintaining termbases for consistency.
TER (Translation Edit Rate)
A metric showing how many edits a human would need to correct a translation.
Token
A unit of text processed by a model, such as a word, subword, or punctuation mark.
Tokenisation in Natural Language Processing
How text is segmented into machine-readable units for NLP pipelines and large language models.
Related Resources
Tokenisation in Natural Language Processing
How text is segmented into machine-readable units for NLP pipelines and large language models.
Federated Learning
A machine learning approach where models are trained across distributed devices or servers without centralising raw data.
Pretraining
The initial phase of training a machine learning model on large datasets before adapting it to specific tasks.
Semantic Similarity
A measure used in natural language processing to determine how similar two texts are in meaning.
Speech Recognition
Technology that converts spoken language into written text using machine learning models.
Supervised Learning
A machine learning method in which models are trained using labelled data.