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VIDEO DOI: https://doi.org/10.48448/b06b-b269

poster

ACL 2024

August 12, 2024

Bangkok, Thailand

Ex\textsuperscript{3}: Automatic Novel Writing by Extracting, Excelsior and Expanding

keywords:

novel generation

long text

llms

large language model

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. Ex\textsuperscript{3} 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 Ex\textsuperscript{3}'s ability to produce higher-quality long-form novels.

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