@inproceedings{salemi-zamani-2025-lamp,
    title = "{L}a{MP}-{QA}: A Benchmark for Personalized Long-form Question Answering",
    author = "Salemi, Alireza  and
      Zamani, Hamed",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.60/",
    doi = "10.18653/v1/2025.emnlp-main.60",
    pages = "1139--1159",
    ISBN = "979-8-89176-332-6",
    abstract = "Personalization is essential for question answering systems that are user-centric. Despite its importance, personalization in answer generation has been relatively underexplored. This is mainly due to lack of resources for training and evaluating personalized question answering systems. We address this gap by introducing LaMP-QA{---}a benchmark designed for evaluating personalized long-form answer generation. The benchmark covers questions from three major categories: (1) Arts {\&} Entertainment, (2) Lifestyle {\&} Personal Development, and (3) Society {\&} Culture, encompassing over 45 subcategories in total. To assess the quality and potential impact of the LaMP-QA benchmark for personalized question answering, we conduct comprehensive human and automatic evaluations, to compare multiple evaluation strategies for evaluating generated personalized responses and measure their alignment with human preferences. Furthermore, we benchmark a number of non-personalized and personalized approaches based on open-source and proprietary large language models. Our results show that incorporating the personalized context provided leads to up to 39{\%} performance improvements. The benchmark is publicly released to support future research in this area."
}