From GPT-1 to GPT-4o: A Deep Dive into the Evolution of Large Language Models

GPT Development Timeline

Since the Transformer architecture debuted in 2017, the NLP field entered the era of pre‑trained language models (PLM). OpenAI’s series of Generative Pre‑Training (GPT) models illustrate how scaling model size, data, and compute has driven rapid capability gains.

2018 – GPT‑1: The Text Learner

Published in June 2018, GPT‑1 introduced the “pre‑train‑fine‑tune” paradigm: a massive unsupervised pre‑training phase on raw text followed by task‑specific fine‑tuning with a small labeled dataset. Its goal was simply to “understand text”.

2019 – GPT‑2: A Multitask Learner

GPT‑2, released in November 2019, expanded the pre‑training corpus and model size dramatically. It was positioned as a “multitask learner” capable of zero‑shot inference—solving tasks without any fine‑tuning—though its zero‑shot performance was still limited.

2020 – GPT‑3: Few‑Shot Mastery

GPT‑3 (175 B parameters) demonstrated two key breakthroughs:

Scaling laws showed that performance improves predictably with model size, data, and compute.

Few‑shot learning, where a prompt containing 3‑5 examples enables the model to outperform traditional fine‑tuned baselines.

Example prompts (shown in the original code block) illustrate zero‑shot and few‑shot sentiment classification:

zero-shot: 请你判断 ‘这真是一个绝佳的机会’ 的情感是正向还是负向，如果是正向，输出 1；否则输出 0

few-shot: 请你判断 ‘这真是一个绝佳的机会’ 的情感是正向还是负向，如果是正向，输出 1；否则输出 0。你可以参考以下示例来判断：‘你的表现非常好’ —— 1；‘太糟糕了’ —— 0；‘真是一个好主意’ —— 1。

2022 – ChartGPT: Chatbot with RLHF

In November 2022, OpenAI released ChartGPT, a chatbot that combined GPT‑3’s few‑shot abilities with instruction‑tuning (SFT) and reinforcement learning from human feedback (RLHF), enabling more natural, instruction‑following interactions.

2023 – GPT‑4: Multimodal Model

GPT‑4 (≈1.76 T parameters) added true multimodal input, allowing image‑text mixed queries and OCR capabilities. Microsoft integrated GPT‑4 across Office products, expanding its reach.

2024 – GPT‑4 Turbo, Sora, and GPT‑4o

2024 saw the release of GPT‑4 Turbo, the text‑to‑video model Sora, and GPT‑4o (omni), which delivers sub‑250 ms audio response times and more natural interaction.

Large Language Models (LLMs)

LLMs are massive transformer‑based models (billions to trillions of parameters) trained on terabytes of text and fine‑tuned with reinforcement learning. Their core capabilities include:

Emergent abilities – capabilities that appear only when model scale crosses a threshold.

In‑context (few‑shot) learning – solving new tasks by providing a few examples in the prompt.

Instruction following – responding correctly to natural‑language commands after instruction‑tuning.

Step‑by‑step reasoning – using chain‑of‑thought prompting to decompose complex problems.

These abilities make LLMs superior to earlier PLMs for a wide range of applications, from code generation to document summarization.

Emergent Abilities

When model size grows, certain skills become pronounced, similar to phase transitions in physics. Larger models exhibit capabilities that smaller ones lack, enabling tasks that were previously impossible.

In‑Context Learning

Introduced with GPT‑3, in‑context learning lets a model understand a task from natural‑language instructions or a handful of examples, eliminating the need for costly fine‑tuning.

Instruction Following

Instruction‑tuned LLMs can execute unseen commands by learning from diverse task descriptions during a supervised fine‑tuning stage, reducing the need for task‑specific training data.

Step‑by‑Step Reasoning

Chain‑of‑thought prompting encourages the model to generate intermediate reasoning steps, improving performance on logical and mathematical problems.

GPT‑2 Decoder‑Only Architecture

The GPT‑2 architecture mirrors GPT‑1 but scales up parameters and switches from post‑norm to pre‑norm (LayerNorm before attention). The processing pipeline is:

Tokenize input text into input_ids.

Embed input_ids and add sinusoidal positional embeddings.

Pass embeddings through a stack of decoder layers (no encoder).

Each decoder layer applies masked multi‑head self‑attention (MHA) after a pre‑LayerNorm, followed by a residual connection and another LayerNorm before the feed‑forward MLP.

The MLP uses two 1‑D convolution kernels (functionally similar to linear layers).

After N decoder layers, project the final hidden states to the vocabulary dimension to generate output tokens.

Key architectural changes—larger depth, pre‑norm, and increased parameter count—help stabilize gradients and enable training of much bigger models.

Illustrative Images