Dragongon/FinLFQA

Tasks

• question-answering
• text-generation

Citation text from the card

@inproceedings{long-etal-2025-finlfqa,
    title = "{F}in{LFQA}: Evaluating Attributed Text Generation of {LLM}s in Financial Long-Form Question Answering",
    author = "Long, Yitao  and
      Hu, Tiansheng  and
      Zhao, Yilun  and
      Cohan, Arman  and
      Zhao, Chen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-emnlp.908/",
    doi = "10.18653/v1/2025.findings-emnlp.908",
    pages = "16730--16750",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Language Models (LLMs) frequently hallucinate to long-form questions, producing plausible yet factually incorrect answers. A common mitigation strategy is to provide attribution to LLM outputs. However, existing benchmarks primarily focus on simple attribution that retrieves supporting textual evidence as references. We argue that in real-world scenarios such as financial applications, attribution goes beyond reference retrieval.We introduce FinLFQA, a benchmark designed to evaluate the ability of LLMs to generate long-form answers to complex financial questions with reliable and nuanced attributions. FinLFQA evaluates three critical aspects of attribution through human annota

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