In this episode, we explore the three Transformer model families that shaped modern NLP and large language models: BERT, GPT, and T5. We explain why they were created, how their architectures differ, and how each one defines a core capability of today’s AI systems.

We show how self-attention moved NLP beyond static word embeddings, enabling deep contextual understanding and large-scale pretraining. From there, we break down how encoder-only, decoder-only, and encoder–decoder models emerged—and why their training objectives matter as much as their architecture.

This episode covers:

• Why early NLP models failed to generalize

• How self-attention enabled contextual language understanding

• BERT and encoder-only models for analysis and comprehension

• GPT and decoder-only models for fluent text generation

• T5 and the text-to-text unification of NLP tasks

• Pretraining objectives: masking, next-token prediction, span corruption

• Scaling laws and emergent abilities

• Instruction tuning and following human intent

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk