RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
Wangchunshu Zhou Yuchen Eleanor Jiang Peng Cui Tiannan Wang Zhenxin Xiao Yifan Hou Ryan Cotterell Mrinmaya Sachan
The fixed-size context of Transformer makes GPT models incapable of
generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a
language-based simulacrum of the recurrence mechanism in RNNs. RecurrentGPT is
built upon a large language model (LLM) such as ChatGPT and uses natural
language to simulate the Long Short-Term Memory mechanism in an LSTM. At each
timestep, RecurrentGPT generates a paragraph of text and updates its
language-based long-short term memory stored on the hard drive and the prompt,
respectively. This recurrence mechanism enables RecurrentGPT to generate texts
of arbitrary length without forgetting. Since human users can easily observe
and edit the natural language memories, RecurrentGPT is interpretable and
enables interactive generation of long text. RecurrentGPT is an initial step
towards next-generation computer-assisted writing systems beyond local editing
suggestions. In addition to producing AI-generated content (AIGC), we also
demonstrate the possibility of using RecurrentGPT as an interactive fiction
that directly interacts with consumers. We call this usage of generative models
by ``AI As Contents'' (AIAC), which we believe is the next form of conventional
AIGC. We further demonstrate the possibility of using RecurrentGPT to create
personalized interactive fiction that directly interacts with readers instead
of interacting with writers. More broadly, RecurrentGPT demonstrates the
utility of borrowing ideas from popular model designs in cognitive science and
deep learning for prompting LLMs. Our code is available at
https://github.com/aiwaves-cn/RecurrentGPT and an online demo is available at
https://www.aiwaves.org/recurrentgpt.