{"ID":2842827,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09310","arxiv_id":"2511.09310","title":"LiteraryTaste: A Preference Dataset for Creative Writing Personalization","abstract":"People have different creative writing preferences, and large language models (LLMs) for these tasks can benefit from adapting to each user's preferences. However, these models are often trained over a dataset that considers varying personal tastes as a monolith. To facilitate developing personalized creative writing LLMs, we introduce LiteraryTaste, a dataset of reading preferences from 60 people, where each person: 1) self-reported their reading habits and tastes (stated preference), and 2) annotated their preferences over 100 pairs of short creative writing texts (revealed preference). With our dataset, we found that: 1) people diverge on creative writing preferences, 2) finetuning a transformer encoder could achieve 75.8% and 67.7% accuracy when modeling personal and collective revealed preferences, and 3) stated preferences had limited utility in modeling revealed preferences. With an LLM-driven interpretability pipeline, we analyzed how people's preferences vary. We hope our work serves as a cornerstone for personalizing creative writing technologies.","short_abstract":"People have different creative writing preferences, and large language models (LLMs) for these tasks can benefit from adapting to each user's preferences. However, these models are often trained over a dataset that considers varying personal tastes as a monolith. To facilitate developing personalized creative writing L...","url_abs":"https://arxiv.org/abs/2511.09310","url_pdf":"https://arxiv.org/pdf/2511.09310v1","authors":"[\"John Joon Young Chung\",\"Vishakh Padmakumar\",\"Melissa Roemmele\",\"Yi Wang\",\"Yuqian Sun\",\"Tiffany Wang\",\"Shm Garanganao Almeda\",\"Brett A. Halperin\",\"Yuwen Lu\",\"Max Kreminski\"]","published":"2025-11-12T13:21:33Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.HC\"]","methods":"[\"Transformer\",\"Large Language Model\",\"Language Model\"]","has_code":false}