Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding
<|reference_start|>Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding: 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 extracts structure information 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.<|reference_end|>
@article{huang2024ex3:,
    title={Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding},
    author={Lei Huang, Jiaming Guo, Guanhua He, Xishan Zhang, Rui Zhang, Shaohui
  Peng, Shaoli Liu, and Tianshi Chen},
    journal={arXiv preprint arXiv:2408.08506},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08506},
    primaryClass={cs.CL cs.AI}
}