What is the difference between BERT and GPT?

BERT is an encoder model optimised for understanding text - classification, sentiment analysis, and search. GPT is a decoder model optimised for generating text. BERT reads bidirectionally; GPT reads left-to-right for autoregressive generation.

Is BERT still relevant in 2024?

Yes, BERT-family models remain excellent for understanding tasks, embedding generation, and classification. They're often faster and cheaper than large generative models for tasks that don't require text generation.

What is masked language modelling?

Masked language modelling is BERT's training approach where random words are hidden (masked) and the model learns to predict them from surrounding context. This teaches deep bidirectional understanding of language.

Can BERT generate text?

BERT is not designed for text generation - it's an encoder model. For generation tasks, use decoder models like GPT or encoder-decoder models like T5. BERT excels at understanding and classification tasks.

Book Free Assessment Calculator

BERT

Bidirectional Encoder Representations from Transformers - a landmark language model from Google that reads text in both directions to understand context.

In-Depth Explanation

BERT, released by Google in 2018, was a breakthrough in natural language understanding that fundamentally changed how machines process language. Unlike previous models that read text left-to-right or right-to-left, BERT reads entire sequences bidirectionally, understanding context from both directions simultaneously.

BERT is an encoder-only transformer model, meaning it excels at understanding text rather than generating it. It was trained using masked language modelling - randomly hiding words and training the model to predict them from context - and next sentence prediction.

Key innovations of BERT include:

Bidirectional context: Understanding words based on both preceding and following text
Pre-training then fine-tuning: A paradigm that made transfer learning practical for NLP
Contextual embeddings: Word representations that change based on surrounding context

BERT remains highly relevant for classification, sentiment analysis, named entity recognition, and generating embeddings for search applications. While GPT models dominate generation tasks, BERT-family models often outperform them on understanding tasks.

Business Context

BERT-based models are commonly used for classification, sentiment analysis, and search relevance ranking in business applications where understanding is more important than generation.

How Clever Ops Uses This

We use BERT-based models extensively for embedding generation in RAG systems and for classification tasks like email routing and document categorisation for our Australian business clients.

Example Use Case

"Using BERT to classify customer support tickets by urgency and topic, automatically routing them to the right team."

Frequently Asked Questions

Learn More

Large Language Models Explained: Complete Business Guide

Understand how LLMs work, compare GPT-4, Claude, Gemini, and Llama, and learn to choose the right model for your business needs. Complete guide to capabilities, limitations, and practical applications.

Read article

Beneficial Ownership Bias (AI)