A Comprehensive History of Large Language Models from the Transformer Era (2017) to DeepSeek‑R1 (2025)

The article provides a detailed retrospective of large language models (LLMs) from the emergence of the Transformer architecture in 2017 to the 2025 launch of DeepSeek‑R1, covering BERT, the GPT series, multimodal models, reasoning models, and the impact on AI research and society.

LLMs are AI systems designed to process, understand, and generate human‑like language by learning patterns from massive datasets, enabling applications such as translation, summarization, chatbots, and content creation.

1. What Is a Language Model?

Language models (LMs) predict the next token in a sequence; large language models (LLMs) are a subset with billions of parameters, offering superior performance across many tasks.

1.1 Large Language Models (LLMs)

LLMs differ from smaller LMs in scale, architecture, training data, and capabilities. The term gained prominence after the Transformer‑based BERT and GPT‑1 papers (2018‑2019) and became widespread after GPT‑3 (2020).

1.2 Autoregressive Language Models

Most LLMs operate autoregressively, predicting the probability distribution of the next token based on preceding text, which enables powerful text generation.

1.3 Generation Capability

Through iterative token prediction, LLMs can generate coherent passages from a prompt, supporting creative writing, dialogue systems, and automated support.

2. The Transformer Revolution (2017)

Vaswani et al. introduced the Transformer in "Attention Is All You Need," overcoming the limitations of RNNs and LSTMs in handling long‑range dependencies and enabling fully parallel computation.

2.1 Key Innovations of the Transformer

Self‑attention allows each token to weigh the relevance of all others, enabling parallelism and better context understanding. Multi‑head attention, feed‑forward networks, layer normalization, and positional encodings further improve performance.

Scalability: full parallelism makes training on massive datasets feasible.

Contextual Understanding: self‑attention captures both local and global dependencies.

3. The Pre‑training Era (2018‑2020)

3.1 BERT: Bidirectional Context

Google's BERT introduced masked language modeling (MLM) and next‑sentence prediction (NSP), achieving state‑of‑the‑art results on many NLP benchmarks.

3.2 GPT: Generative Pre‑training

OpenAI's GPT series used a decoder‑only Transformer for autoregressive generation. GPT‑2 (2019) demonstrated impressive zero‑shot abilities, while GPT‑3 (2020) scaled to 175 B parameters, showing strong few‑shot and zero‑shot performance across tasks.

3.3 Impact of Scale

Increasing model size, dataset size, and compute resources consistently improved language modeling performance, highlighting the importance of scale.

4. Alignment and Post‑Training (2021‑2022)

Supervised fine‑tuning (SFT) and reinforcement learning from human feedback (RLHF) were introduced to align LLM outputs with human preferences and reduce hallucinations.

4.1 Supervised Fine‑tuning (SFT)

SFT trains models on high‑quality input‑output pairs to follow instructions, but it is labor‑intensive and limited in generalization.

4.2 RLHF

RLHF trains a reward model from human rankings of model outputs and then uses PPO to fine‑tune the LLM, improving alignment and reliability.

4.3 ChatGPT

ChatGPT (2022) combined instruction‑tuned dialogue data with RLHF, delivering conversational AI that sparked widespread public adoption and ethical discussions.

5. Multimodal Models (2023‑2024)

Models such as GPT‑4V and GPT‑4o integrated vision, audio, and video with language, enabling richer interactions in healthcare, education, and creative domains.

6. Open‑Source and Open‑Weight Models (2023‑2024)

Open‑weight LLMs (e.g., LLaMA, Mistral) and fully open‑source models (e.g., OPT, BERT) democratized access to advanced AI, fostering community‑driven innovation.

7. Reasoning Models (2024)

OpenAI's o1 series introduced long‑chain‑of‑thought (Long CoT) reasoning, allowing models to decompose problems, self‑criticize, and explore alternatives, achieving near‑human performance on math and coding benchmarks.

8. Cost‑Effective Inference Models: DeepSeek‑R1 (2025)

DeepSeek‑R1 leverages a mixture‑of‑experts (MoE) architecture, multi‑head latent attention, and multi‑token prediction to deliver high performance at a fraction of the cost of Western LLMs.

8.1 DeepSeek‑V3 (2024‑12)

DeepSeek‑V3 features MLA, DeepSeekMoE, and MTP, achieving comparable quality to top‑tier models while costing roughly 1/30 of the price.

8.2 DeepSeek‑R1‑Zero and DeepSeek‑R1 (2025‑01)

R1‑Zero eliminates SFT, using rule‑based RL (GRPO) for efficient training. R1 adds a small curated dataset and additional RL stages to improve readability and alignment.

8.3 Industry Impact

The affordable, open‑weight DeepSeek‑R1 is expected to accelerate AI adoption across sectors, with major cloud providers already offering the model.

Conclusion

From the 2017 Transformer to the 2025 DeepSeek‑R1, LLMs have transformed AI, driven by scaling, alignment, multimodality, and cost‑efficiency, paving the way for more inclusive and powerful AI systems.

Disclaimer: The content reflects the author’s perspective and cites sources where applicable. Any copyright issues should be reported for removal.

Promotional Notice: The article also advertises a bundled collection of technical e‑books and resources for architects, offering free PDF versions to prior purchasers and a discounted price of ¥259 (originally ¥499) for new customers.