@inproceedings{yang-etal-2022-re3,
title = "Re3: Generating Longer Stories With Recursive Reprompting and Revision",
author = "Yang, Kevin and
Tian, Yuandong and
Peng, Nanyun and
Klein, Dan",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.296",
doi = "10.18653/v1/2022.emnlp-main.296",
pages = "4393--4479",
abstract = "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{\%}).",
}
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<abstract>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%).</abstract>
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%0 Conference Proceedings
%T Re3: Generating Longer Stories With Recursive Reprompting and Revision
%A Yang, Kevin
%A Tian, Yuandong
%A Peng, Nanyun
%A Klein, Dan
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F yang-etal-2022-re3
%X 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%).
%R 10.18653/v1/2022.emnlp-main.296
%U https://aclanthology.org/2022.emnlp-main.296
%U https://doi.org/10.18653/v1/2022.emnlp-main.296
%P 4393-4479
Markdown (Informal)
[Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://aclanthology.org/2022.emnlp-main.296) (Yang et al., EMNLP 2022)
ACL