{
 "id": "tartuNLP/winogrande_et",
 "owner": "tartuNLP",
 "name": "winogrande_et",
 "downloads": 401,
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 "last_modified": "2026-08-17T16:47:17.000Z",
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 "gated": false,
 "tasks": [
  "text-classification"
 ],
 "citation": "@inproceedings{ojastu-etal-2026-estonian,\n  title = {{Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation}},\n  author = {Ojastu, Marii and Kuulmets, Hele-Andra and Dorkin, Aleksei and Borovikova, Marika and Särg, Dage and Sirts, Kairit},\n  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},\n  month = {May},\n  year = {2026},\n  pages = {6904--6918},\n  address = {Palma, Mallorca, Spain},\n  publisher = {European Language Resources Association (ELRA)},\n  editor = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio},\n  doi = {10.63317/27gw7fzp2jbx},\n  abstract = {In this paper, we present a localized and culturally adapted Estonian translation of the test set from the widely used commonsense reasoning benchmark, WinoGrande. We detail the translation and adaptation process carried out by translation specialists and evaluate the performance of both proprietary and open source models on the human translated benchmark. Additionally, we explore the feasibility of achieving high-quality machine translation by incorporating insights from the manual translation process into the design of a detailed prompt. This prompt is specifically tailored to address both the linguistic characteristics of Estonian and the unique translation challenges posed by the WinoGrande dataset. Our findings show that model performance on the human translated E",
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