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        <title>A Report on LSG 2024: LLM Fine-Tuning for Fictional Stories Generation</title>
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            <namePart type="family">Mille</namePart>
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        <name type="personal">
            <namePart type="given">Miruna-Adriana</namePart>
            <namePart type="family">Clinciu</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Report on LSG 2024: LLM Fine-Tuning for Fictional Stories Generation
%A Seredina, Daria
%Y Mille, Simon
%Y Clinciu, Miruna-Adriana
%S Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F seredina-2024-report
%X 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.
%U https://aclanthology.org/2024.inlg-genchal.14
%P 123-127

Markdown (Informal)

[A Report on LSG 2024: LLM Fine-Tuning for Fictional Stories Generation](https://aclanthology.org/2024.inlg-genchal.14) (Seredina, INLG 2024)

• A Report on LSG 2024: LLM Fine-Tuning for Fictional Stories Generation (Seredina, INLG 2024)

ACL

• Daria Seredina. 2024. A Report on LSG 2024: LLM Fine-Tuning for Fictional Stories Generation. In Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges, pages 123–127, Tokyo, Japan. Association for Computational Linguistics.