PaperBridge

Transformer paper reading path

Understand modern AI from the original Transformer paper

Attention Is All You Need replaced recurrence in sequence models with attention, allowing positions to interact directly and training to run more in parallel. The paper demonstrated this on machine translation; it did not by itself establish every later large-language-model capability. BERT, GPT-3, Vision Transformer, and multimodal research extended that foundation step by step.

PaperBridge Editorial·July 21, 2026

A six-paper Transformer path

Start with attention

Trace tokens, positional encodings, self-attention, feed-forward layers, and the encoder–decoder before deriving every equation.

Add pre-training and scale

BERT demonstrates bidirectional pre-training; GPT-3 demonstrates scale and in-context learning on a decoder-only architecture.

Extend and adapt

ViT treats image patches as tokens, LoRA lowers adaptation cost, and LLaVA connects visual and language representations.

Key papers in the Transformer lineage

Read in this order to separate the original attention architecture from capabilities demonstrated by later language, vision, and multimodal systems.

  1. 01

    2017 · Ashish Vaswani et al.

    Attention Is All You Need

    The Transformer replaced recurrence with attention and became the shared backbone of modern foundation models.

    PaperBridge Original paper
  2. 02

    2018 · Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

    It established bidirectional pre-training followed by task fine-tuning as a general NLP recipe.

    PaperBridge Original paper
  3. 03

    2020 · Tom B. Brown et al.

    Language Models are Few-Shot Learners

    It demonstrated broad few-shot task learning through prompting at unprecedented scale.

    PaperBridge Original paper
  4. 04

    2020 · Alexey Dosovitskiy et al.

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    It showed that a pure Transformer over image patches could outperform convolutional networks at scale.

    PaperBridge Original paper
  5. 05

    2021 · Edward J. Hu et al.

    LoRA: Low-Rank Adaptation of Large Language Models

    It adapted large models through small low-rank updates instead of retraining every weight.

    PaperBridge Original paper
  6. 06

    2023 · Haotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae Lee

    Visual Instruction Tuning

    It connected a vision encoder and a language model through visual instruction tuning to build a conversational assistant.

    PaperBridge Original paper

FAQ

Transformer paper FAQ

Is self-attention the same as a Transformer?

No. Self-attention is a central module; a full Transformer also includes multi-head attention, positional information, feed-forward networks, residual connections, and normalization.

What did Attention Is All You Need actually prove?

It showed that an attention-only sequence transduction model could improve machine-translation quality while enabling substantially more parallel training.

Are BERT and GPT both Transformers?

Yes. BERT mainly uses a bidirectional encoder, while GPT models use an autoregressive decoder that predicts later tokens from earlier context.