Transformers Without the RNN

2026-04-29 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

The Transformer architecture, introduced in the 2017 paper "Attention Is All You Need," replaced recurrent neural networks (RNNs) with self-attention for parallel processing. This architecture quickly diverged into encoder-only models like BERT and decoder-only models like GPT, with most major models since adopting one half. An encoder compresses input tokens into dense numerical vectors, capturing meaning through multiple layers of self-attention and feed-forward networks, exemplified by BERT's 12 stacked layers. Encoder-only models are used for understanding tasks such as text classification and sentiment analysis, typically consisting of a pretrained, task-independent body and a task-specific head. A decoder generates outputs, often one token at a time, using masked self-attention to only attend to past tokens. The core difference in decoder code is a causal mask that prevents tokens from seeing future information, enabling autoregressive generation. While the original Transformer used both for tasks like translation, most modern applications utilize either an encoder for understanding or a decoder for generation.

Key takeaway

For AI Scientists and Machine Learning Engineers designing or selecting large language models, understanding the fundamental distinction between encoder and decoder architectures is crucial. Your choice hinges on whether your primary task is text understanding (e.g., classification, sentiment analysis) or text generation (e.g., summarization, chatbots). Opt for encoder-only models like BERT for interpretation tasks, and decoder-only models like GPT for generative tasks, recognizing that the core difference lies in a few lines of masking code.

Key insights

The Transformer architecture's split into encoder-only and decoder-only models is driven by their distinct "understanding" versus "generating" functions.

Principles

• Encoders provide bidirectional context.
• Decoders enforce causal, autoregressive generation.
• Pretrained bodies adapt via task-specific heads.

Method

Encoder-only models stack self-attention and feed-forward layers to produce contextual embeddings, which are then fed into a task-specific head for interpretation tasks like classification.

In practice

• Use `AutoModel.from_pretrained` for encoder body.
• Use `AutoModelForSequenceClassification` for body+head.
• Apply causal masks for autoregressive generation.

Topics

• Transformers
• Self-Attention
• Encoder-Decoder Architecture
• Causal Masking
• Encoder-Only Models

Best for: AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.