@inproceedings{benyoash-etal-2025-secque,
    title = "{SECQUE}: A Benchmark for Evaluating Real-World Financial Analysis Capabilities",
    author = "BenYoash, Noga  and
      Brief, Menachem  and
      Ovadia, Oded  and
      Shenderovitz, Gil  and
      Mishaeli, Moshik  and
      Lemberg, Rachel  and
      Sheetrit, Eitam",
    editor = "Arviv, Ofir  and
      Clinciu, Miruna  and
      Dhole, Kaustubh  and
      Dror, Rotem  and
      Gehrmann, Sebastian  and
      Habba, Eliya  and
      Itzhak, Itay  and
      Mille, Simon  and
      Perlitz, Yotam  and
      Santus, Enrico  and
      Sedoc, Jo{\~a}o  and
      Shmueli Scheuer, Michal  and
      Stanovsky, Gabriel  and
      Tafjord, Oyvind",
    booktitle = "Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM{\texttwosuperior})",
    month = jul,
    year = "2025",
    address = "Vienna, Austria and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/nschneid-patch-1/2025.gem-1.16/",
    pages = "212--230",
    ISBN = "979-8-89176-261-9",
    abstract = "We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models' performance on our benchmark. By making SECQUE publicly available (https://huggingface.co/datasets/nogabenyoash/SecQue), we aim to facilitate further research and advancements in financial AI."
}