{
 "id": "Dragongon/FinLFQA",
 "owner": "Dragongon",
 "name": "FinLFQA",
 "downloads": 329,
 "likes": 0,
 "last_modified": "2026-04-09T20:13:11.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "question-answering",
  "text-generation"
 ],
 "citation": "@inproceedings{long-etal-2025-finlfqa,\n    title = \"{F}in{LFQA}: Evaluating Attributed Text Generation of {LLM}s in Financial Long-Form Question Answering\",\n    author = \"Long, Yitao  and\n      Hu, Tiansheng  and\n      Zhao, Yilun  and\n      Cohan, Arman  and\n      Zhao, Chen\",\n    editor = \"Christodoulopoulos, Christos  and\n      Chakraborty, Tanmoy  and\n      Rose, Carolyn  and\n      Peng, Violet\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2025\",\n    month = nov,\n    year = \"2025\",\n    address = \"Suzhou, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.findings-emnlp.908/\",\n    doi = \"10.18653/v1/2025.findings-emnlp.908\",\n    pages = \"16730--16750\",\n    ISBN = \"979-8-89176-335-7\",\n    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",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "22900e97366d0b977b6f37863486bc9828e5bdc0",
 "stated_evidence": "https://huggingface.co/datasets/Dragongon/FinLFQA/blob/22900e97366d0b977b6f37863486bc9828e5bdc0/README.md",
 "file_evidence": "https://huggingface.co/datasets/Dragongon/FinLFQA/tree/22900e97366d0b977b6f37863486bc9828e5bdc0",
 "card_nc": false
}