From RNN to ChatGPT: How AIGC Evolved with Transformers and Large Models

AIGC Overview

In the digital age, content creation has shifted from traditional PGC and UGC to AIGC (AI‑generated content), which applies AI techniques to produce text, images, audio/video, and even software.

Origins of AIGC

AIGC began with RNN‑based Seq2Seq models, which consist of an encoder and a decoder. Early attempts suffered from poor text quality, frequent grammatical errors, and unclear semantics.

Rise of the Transformer

In 2017, the Transformer architecture dramatically improved training efficiency and the ability to capture complex feature representations, leading to pretrained models such as BERT and the GPT series becoming dominant in AIGC.

UniLM: A Unified Language Model

Microsoft introduced UniLM in 2019, a BERT‑based generation model that omits a separate decoder, achieving strong text‑generation performance while retaining BERT’s representation power.

T5: Text‑to‑Text Transfer Transformer

Google’s 2020 T5 model reframes every NLP task as a text‑to‑text problem, simplifying training and deployment by using a single architecture and training strategy for all tasks.

BART

Facebook’s 2020 BART combines a bidirectional encoder (like BERT) with an autoregressive decoder (like GPT), making it more suitable for text generation and providing richer bidirectional context than GPT alone.

GPT Series: From GPT‑1 to ChatGPT

OpenAI’s GPT series demonstrates the power of scaling data and model size. GPT‑1 (2018) introduced generative pre‑training on large unlabeled corpora. GPT‑2 (2019) removed fine‑tuning layers, expanded the vocabulary, and trained on 40 GB of data, improving generalization. GPT‑3 (2020) scaled to 175 billion parameters and 45 TB of data, enabling zero‑ and few‑shot learning.

InstructGPT (2022) added reinforcement learning from human feedback (RLHF) to address unsafe or unhelpful outputs. ChatGPT, released in November 2022, fine‑tuned GPT‑3.5 with the same RLHF pipeline, further improving instruction following and reducing harmful generation.

Conclusion

The progress of AIGC—from RNN‑based models to advanced Transformer architectures—has provided increasingly powerful tools for understanding and generating content. Continued evolution promises further innovation and value.

References: How Does ChatGPT Work? Tracing The Evolution Of AIGC – https://www.dtonomy.com/how-does-chatgpt-work/