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
this https URL and an online demo is available at
this https URL

这篇论文介绍了 RecurrentGPT，它是一种神经网络模型，使用自然语言来模拟 LSTM 中的长短期记忆机制，从而实现生成任意长度的文本。不仅能生成 AI 生成内容（AIGC），还可以作为交互式虚构小说，直接与消费者进行互动， 是下一代计算机辅助写作系统的重要一步。