@inproceedings{grundmann-etal-2026-clinibench,
    title = "{C}lini{B}ench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models",
    author = {Grundmann, Paul  and
      Frick, Jan  and
      Fast, Dennis  and
      Steffek, Thomas  and
      Gers, Felix  and
      Nejdl, Wolfgang  and
      L{\"o}ser, Alexander},
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.247/",
    pages = "5360--5378",
    ISBN = "979-8-89176-380-7",
    abstract = "With their growing capabilities, generative large language models (LLMs) are being increasingly investigated for complex medical tasks.However, their effectiveness in real-world clinical applications remains underexplored. To address this, we present CliniBench, the first benchmark that enables comparability of well-studied encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in the MIMIC-IV dataset. Our extensive study compares 12 generative LLMs and 3 encoder-based classifiers and demonstrates that encoder-based classifiers consistently outperform generative models in diagnosis prediction. We assess several retrieval augmentation strategies for in-context learning from similar patients and find that they provide notable performance improvements for generative LLMs."
}