@inproceedings{trienes-etal-2025-marcel,
    title = "Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support",
    author = {Trienes, Jan  and
      Derzhanskaia, Anastasiia  and
      Schwarzkopf, Roland  and
      M{\"u}hling, Markus  and
      Schl{\"o}tterer, J{\"o}rg  and
      Seifert, Christin},
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-zheyuan-zhang-jhu/2025.emnlp-demos.13/",
    doi = "10.18653/v1/2025.emnlp-demos.13",
    pages = "181--195",
    ISBN = "979-8-89176-334-0",
    abstract = "We present Marcel, a lightweight and open-source conversational agent designed to support prospective students with admission-related inquiries. The system aims to provide fast and personalized responses, while reducing workload of university staff. We employ retrieval-augmented generation to ground answers in university resources and to provide users with verifiable, contextually relevant information. We introduce a Frequently Asked Question (FAQ) retriever that maps user questions to knowledge-base entries, which allows administrators to steer retrieval, and improves over standard dense/hybrid retrieval strategies. The system is engineered for easy deployment in resource-constrained academic settings. We detail the system architecture, provide a technical evaluation of its components, and report insights from a real-world deployment."
}