@inproceedings{denecke-2026-novice,
    title = "From Novice to Expert: Generating Audience-Dependent Concert Moderations with {RAG}-{LLM}s",
    author = "Denecke, Kerstin",
    editor = "Epure, Elena V.  and
      Oramas, Sergio  and
      Doh, SeungHeon  and
      Ramoneda, Pedro  and
      Kruspe, Anna  and
      Sordo, Mohamed",
    booktitle = "Proceedings of the 4th Workshop on {NLP} for Music and Audio ({NLP}4{M}us{A} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.nlp4musa-1.1/",
    pages = "1--6",
    ISBN = "979-8-89176-369-2",
    abstract = "In this paper, we study the capabilities of large language models (LLMs) to adapt a concert moderation to diverse expertise levels of listeners. Our proof-of-concept concert moderator is based on retrieval-augmented generation (RAG) and uses few-shot audience modelling to infer listener{'}s expertise. We study the capabilities of the system to adapt to three different listener{'}s expertise levels. Two open domain LLMs are compared: gpt-oss:20b and llama3. The recognised differences among the models suggest that they vary in how directly they reproduce versus paraphrase retrieved information while maintaining semantic alignment."
}