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        <title>Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing</title>
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        <namePart type="given">Isaac</namePart>
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        <namePart type="given">Lucie-Aimée</namePart>
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        <namePart type="given">Miriam</namePart>
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            <title>Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia</title>
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            <namePart type="given">Lucie</namePart>
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        <name type="personal">
            <namePart type="given">Angela</namePart>
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        <name type="personal">
            <namePart type="given">Tajuddeen</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
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    <abstract>Wikimedia content is used extensively by the AI community and within the language modeling community in particular. In this paper, we provide a review of the different ways in which Wikimedia data is curated to use in NLP tasks across pre-training, post-training, and model evaluations. We point to opportunities for greater use of Wikimedia content but also identify ways in which the language modeling community could better center the needs of Wikimedia editors. In particular, we call for incorporating additional sources of Wikimedia data, a greater focus on benchmarks for LLMs that encode Wikimedia principles, and greater multilingualism in Wikimedia-derived datasets.</abstract>
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            <start>91</start>
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%0 Conference Proceedings
%T Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing
%A Johnson, Isaac
%A Kaffee, Lucie-Aimée
%A Redi, Miriam
%Y Lucie-Aimée, Lucie
%Y Fan, Angela
%Y Gwadabe, Tajuddeen
%Y Johnson, Isaac
%Y Petroni, Fabio
%Y van Strien, Daniel
%S Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F johnson-etal-2024-wikimedia
%X Wikimedia content is used extensively by the AI community and within the language modeling community in particular. In this paper, we provide a review of the different ways in which Wikimedia data is curated to use in NLP tasks across pre-training, post-training, and model evaluations. We point to opportunities for greater use of Wikimedia content but also identify ways in which the language modeling community could better center the needs of Wikimedia editors. In particular, we call for incorporating additional sources of Wikimedia data, a greater focus on benchmarks for LLMs that encode Wikimedia principles, and greater multilingualism in Wikimedia-derived datasets.
%U https://aclanthology.org/2024.wikinlp-1.14
%P 91-101

Markdown (Informal)

[Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing](https://aclanthology.org/2024.wikinlp-1.14) (Johnson et al., WikiNLP 2024)

• Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing (Johnson et al., WikiNLP 2024)

ACL

• Isaac Johnson, Lucie-Aimée Kaffee, and Miriam Redi. 2024. Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing. In Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia, pages 91–101, Miami, Florida, USA. Association for Computational Linguistics.