We consider the problem of automatically generating longer stories of over
two thousand words. Compared to prior work on shorter stories, long-range plot
coherence and relevance are more central challenges here. We propose the
Recursive Reprompting and Revision framework (Re3) to address these challenges
by (a) prompting a general-purpose language model to construct a structured
overarching plan, and (b) generating story passages by repeatedly injecting
contextual information from both the plan and current story state into a
language model prompt. We then revise by (c) reranking different continuations
for plot coherence and premise relevance, and finally (d) editing the best
continuation for factual consistency. Compared to similar-length stories
generated directly from the same base model, human evaluators judged
substantially more of Re3's stories as having a coherent overarching plot (by
14% absolute increase), and relevant to the given initial premise (by 20%).

本文介绍了如何使用 Recursive Reprompting 和 Revision 框架 (Re3) 来自动生成长篇故事。该方法使用通用语言模型构建结构化总体计划，并在当前情境信息注入语言模型之前，反复注入来自计划和当前故事状态的情境信息。之后，通过重新排列连续文本并编辑最佳连续文本以获取事实一致性来进行修订。该方法生成的故事比直接从相同基础模型生成的类似长度故事更具条理性和相关性。