@inproceedings{seredina-2024-report,
    title = "A Report on {LSG} 2024: {LLM} Fine-Tuning for Fictional Stories Generation",
    author = "Seredina, Daria",
    editor = "Mille, Simon  and
      Clinciu, Miruna-Adriana",
    booktitle = "Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges",
    month = sep,
    year = "2024",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.inlg-genchal.14/",
    doi = "10.18653/v1/2024.inlg-genchal.14",
    pages = "123--127",
    abstract = "Our methodology centers around fine-tuning a large language model (LLM), leveraging supervised learning to produce fictional text. Our model was trained on a dataset crafted from a collection of public domain books sourced from Project Gutenberg, which underwent thorough processing. The final fictional text was generated in response to a set of prompts provided in the baseline. Our approach was evaluated using a combination of automatic and human assessments, ensuring a comprehensive evaluation of our model{'}s performance."
}