RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text

Authors: Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui, Tiannan Wang, Zhenxin Xiao, Yifan Hou, Ryan Cotterell, Mrinmaya Sachan

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License: CC BY 4.0

Results of the summarizing process for the arXiv paper: 2305.13304v1

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In this paper, the authors introduce RecurrentGPT, a language-based model that enables the generation of arbitrarily long text without forgetting. Unlike hierarchical methods that prompt LLMs for long story generation in a plan-and-write fashion, RecurrentGPT overcomes limitations via recurrent generation, enabling effective human-LM collaboration and improving flexibility and controllability for long text generation. The authors also discuss the limitations of existing AI writing assistants and automatic generation systems and how RecurrentGPT can improve the quality of AI-generated long texts while increasing the productivity of human writers. They further demonstrate the possibility of using RecurrentGPT as an interactive fiction that directly interacts with consumers, which they call "AI As Contents" (AIAC). The authors provide evaluations of RecurrentGPT's performance in generating novel genres such as sci-fi and fantasy, as well as discussing potential social impacts and promoting responsible usage of generative AI.
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Abstract: 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.

Submitted to arXiv on 22 May. 2023

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