@inproceedings{lei-etal-2024-ex3,
title = "Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding",
author = "Lei, Huang and
Guo, Jiaming and
He, Guanhua and
Zhang, Xishan and
Zhang, Rui and
Peng, Shaohui and
Liu, Shaoli and
Chen, Tianshi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.luhme-long.494/",
doi = "10.18653/v1/2024.acl-long.494",
pages = "9125--9146",
abstract = "Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarchical framework that first plans and then writes. Despite the fact that the generated novels reach a sufficient length, they exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. In this paper, we propose a method named Extracting Excelsior and Expanding. Ex3 initially extract structural information by learning from raw novel data. By combining this structure information with the novel data, an instruction-following dataset is meticulously crafted. This dataset is then utilized to fine-tune the LLM, aiming for excelsior generation performance. In the final stage, a tree-like expansion method is deployed to facilitate the generation of arbitrarily long novels.Evaluation against previous methods showcases Ex3`s ability to produce higher-quality long-form novels."
}
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<abstract>Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarchical framework that first plans and then writes. Despite the fact that the generated novels reach a sufficient length, they exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. In this paper, we propose a method named Extracting Excelsior and Expanding. Ex3 initially extract structural information by learning from raw novel data. By combining this structure information with the novel data, an instruction-following dataset is meticulously crafted. This dataset is then utilized to fine-tune the LLM, aiming for excelsior generation performance. In the final stage, a tree-like expansion method is deployed to facilitate the generation of arbitrarily long novels.Evaluation against previous methods showcases Ex3‘s ability to produce higher-quality long-form novels.</abstract>
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%0 Conference Proceedings
%T Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding
%A Lei, Huang
%A Guo, Jiaming
%A He, Guanhua
%A Zhang, Xishan
%A Zhang, Rui
%A Peng, Shaohui
%A Liu, Shaoli
%A Chen, Tianshi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F lei-etal-2024-ex3
%X Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarchical framework that first plans and then writes. Despite the fact that the generated novels reach a sufficient length, they exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. In this paper, we propose a method named Extracting Excelsior and Expanding. Ex3 initially extract structural information by learning from raw novel data. By combining this structure information with the novel data, an instruction-following dataset is meticulously crafted. This dataset is then utilized to fine-tune the LLM, aiming for excelsior generation performance. In the final stage, a tree-like expansion method is deployed to facilitate the generation of arbitrarily long novels.Evaluation against previous methods showcases Ex3‘s ability to produce higher-quality long-form novels.
%R 10.18653/v1/2024.acl-long.494
%U https://aclanthology.org/2024.luhme-long.494/
%U https://doi.org/10.18653/v1/2024.acl-long.494
%P 9125-9146
Markdown (Informal)
[Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding](https://aclanthology.org/2024.luhme-long.494/) (Lei et al., ACL 2024)
ACL
- Huang Lei, Jiaming Guo, Guanhua He, Xishan Zhang, Rui Zhang, Shaohui Peng, Shaoli Liu, and Tianshi Chen. 2024. Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9125–9146, Bangkok, Thailand. Association for Computational Linguistics.