{
 "id": "AMR-KELEG/DLAMA-v1",
 "owner": "AMR-KELEG",
 "name": "DLAMA-v1",
 "downloads": 34,
 "likes": 0,
 "last_modified": "2025-07-20T12:04:09.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
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 "file_licence": null,
 "licence_files": [],
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 "gated": false,
 "tasks": [],
 "citation": "@inproceedings{keleg-magdy-2023-dlama,\n    title = \"{DLAMA}: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language Models\",\n    author = \"Keleg, Amr  and\n      Magdy, Walid\",\n    editor = \"Rogers, Anna  and\n      Boyd-Graber, Jordan  and\n      Okazaki, Naoaki\",\n    booktitle = \"Findings of the Association for Computational Linguistics: ACL 2023\",\n    month = jul,\n    year = \"2023\",\n    address = \"Toronto, Canada\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2023.findings-acl.389/\",\n    doi = \"10.18653/v1/2023.findings-acl.389\",\n    pages = \"6245--6266\",\n    abstract = \"A few benchmarking datasets have been released to evaluate the factual knowledge of pretrained language models. These benchmarks (e.g., LAMA, and ParaRel) are mainly developed in English and later are translated to form new multilingual versions (e.g., mLAMA, and mParaRel). Results on these multilingual benchmarks suggest that using English prompts to recall the facts from multilingual models usually yields significantly better and more consistent performance than using non-English prompts. Our analysis shows that mLAMA is biased toward facts from Western countries, which might affect the fairness of probing models. We propose a new framework for curating factual triples from Wikidata that are culturally diverse. A new benchmark DLAMA-v1 is built of factual triples from three pairs of contrasting cultures having a ",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive.",
 "revision": "6608f4ae59071f588f6c5fa5c77bca792301a9c8",
 "stated_evidence": "https://huggingface.co/datasets/AMR-KELEG/DLAMA-v1/blob/6608f4ae59071f588f6c5fa5c77bca792301a9c8/README.md",
 "file_evidence": "https://huggingface.co/datasets/AMR-KELEG/DLAMA-v1/tree/6608f4ae59071f588f6c5fa5c77bca792301a9c8",
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}