@inproceedings{wen-etal-2023-grove,
title = "{GROVE}: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence",
author = "Wen, Zhihua and
Tian, Zhiliang and
Wu, Wei and
Yang, Yuxin and
Shi, Yanqi and
Huang, Zhen and
Li, Dongsheng",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.262",
doi = "10.18653/v1/2023.findings-emnlp.262",
pages = "3980--3998",
abstract = "Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce (GROVE) to enhance stories{'} complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an {``}asking-why{''} prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative{'}s complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.",
}
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<abstract>Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce (GROVE) to enhance stories’ complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an “asking-why” prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative’s complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence
%A Wen, Zhihua
%A Tian, Zhiliang
%A Wu, Wei
%A Yang, Yuxin
%A Shi, Yanqi
%A Huang, Zhen
%A Li, Dongsheng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wen-etal-2023-grove
%X Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce (GROVE) to enhance stories’ complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an “asking-why” prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative’s complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.
%R 10.18653/v1/2023.findings-emnlp.262
%U https://aclanthology.org/2023.findings-emnlp.262
%U https://doi.org/10.18653/v1/2023.findings-emnlp.262
%P 3980-3998
Markdown (Informal)
[GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence](https://aclanthology.org/2023.findings-emnlp.262) (Wen et al., Findings 2023)
ACL