@article{tayal-etal-2026-conversational,
    title = "Conversational Assistants to Support Patients with Heart Failure: Comparing a Neurosymbolic Architecture with {GPT}",
    author = "Tayal, Anuja  and
      Salunke, Devika  and
      Di Eugenio, Barbara  and
      Allen-Meares, Paula G.  and
      Abril, Eulalia P.  and
      Garcia-Bedoya, Olga  and
      Dickens, Carolyn A.  and
      Boyd, Andrew D.",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.228/",
    pages = "2913--2926",
    abstract = "Conversational assistants are becoming increasingly popular, including in healthcare, partly due to the availability and capabilities of Large Language Models. There is a need for controlled, probing evaluations with real stakeholders, which can highlight the advantages and disadvantages of more traditional architectures and those based on generative AI. We present a within-group user study to compare two versions of a conversational assistant that allows patients with heart failure to ask about the salt content in food. One version of the system was developed with a neurosymbolic architecture, and another is based on GPT. Our objective in evaluating the two dialogue systems was not only to compare task performance but also to gain insights from real stakeholders. Results indicate that the two systems complement each other, highlighting the promise of a hybrid approach that leverages the strengths of both systems."
}