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README.md
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pretty_name: DemosQA
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size_categories:
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- n<1K
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---
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# DemosQA
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We introduce DemosQA, a novel Greek QA dataset, which is constructed using social media user questions and community-reviewed answers to better capture the Greek social and cultural zeitgeist.
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It comprises questions extracted from the “r/greece” subreddit, each accompanied by four candidate answers, the selected best answer and its index, the date of posting, and the corresponding Reddit post ID.
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Candidate answers are ranked based on community voting, with the highest-upvoted response designated as the reference answer.
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This community-driven ranking mechanism not only ensures that the dataset captures genuine user preferences but also establishes a meaningful benchmark for assessing how closely large language models align with human judgments of response quality.
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For information about dataset creation, limitations etc. see the
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### Supported Task
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from datasets import load_dataset
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# Load the dataset.
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test_split = load_dataset('IMISLab/DemosQA', split = '
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print(test_split[0])
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```
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## Contact
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## Citation
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```
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```
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pretty_name: DemosQA
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size_categories:
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- n<1K
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configs:
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- config_name: default
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data_files:
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- split: test
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path: "DemosQA.csv"
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---
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# DemosQA
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We introduce DemosQA (δῆμος), a novel Greek QA dataset, which is constructed using social media user questions and community-reviewed answers to better capture the Greek social and cultural zeitgeist.
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It comprises questions extracted from the “r/greece” subreddit, each accompanied by four candidate answers, the selected best answer and its index, the date of posting, and the corresponding Reddit post ID.
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Candidate answers are ranked based on community voting, with the highest-upvoted response designated as the reference answer.
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This community-driven ranking mechanism not only ensures that the dataset captures genuine user preferences but also establishes a meaningful benchmark for assessing how closely large language models align with human judgments of response quality.
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For information about dataset creation, limitations etc. see the [arxiv preprint](https://arxiv.org/abs/2602.16811).
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<img src="demosqa.png" width="400"/>
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### Supported Task
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from datasets import load_dataset
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# Load the dataset.
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test_split = load_dataset('IMISLab/DemosQA', split = 'test')
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print(test_split[0])
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```
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## Contact
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## Citation
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```
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@misc{
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mastrokostas2026evaluatingmonolingualmultilinguallarge,
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title = {Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark},
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author = {Charalampos Mastrokostas and Nikolaos Giarelis and Nikos Karacapilidis},
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year = {2026},
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eprint = {2602.16811},
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archivePrefix = {arXiv},
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primaryClass = {cs.CL},
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url = {https://arxiv.org/abs/2602.16811},
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}
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```
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