[2401.06408] AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters

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However, decisions around what data is retained or removed during this initial stage are under-scrutinized. In our work, we ground web text, which is a popular pretraining data source, to its social and geographic contexts. We create a new dataset of 10.3 million self-descriptions of website creators, and extract information about who they are and where they are from: their topical interests, social roles, and geographic affiliations. Then, we conduct the first study investigating how ten "quality" and English language identification (langID) filters affect webpages that vary along these social dimensions. Our experiments illuminate a range of implicit preferences in data curation: we show that some quality classifiers act like topical domain filters, and langID can overlook English content from some regions of the world. 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However, decisions around what data is retained or removed during this initial stage are under-scrutinized. In our work, we ground web text, which is a popular pretraining data source, to its social and geographic contexts. We create a new dataset of 10.3 million self-descriptions of website creators, and extract information about who they are and where they are from: their topical interests, social roles, and geographic affiliations. Then, we conduct the first study investigating how ten "quality" and English language identification (langID) filters affect webpages that vary along these social dimensions. Our experiments illuminate a range of implicit preferences in data curation: we show that some quality classifiers act like topical domain filters, and langID can overlook English content from some regions of the world. Overall, we hope that our work will encourage a new line of research on pretraining data curation practices and its social implications." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" href="{url_path('ignore_me')}"></a> <div 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class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&query=Lucy,+L" rel="nofollow">Li Lucy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gururangan,+S" rel="nofollow">Suchin Gururangan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Soldaini,+L" rel="nofollow">Luca Soldaini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Strubell,+E" rel="nofollow">Emma Strubell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bamman,+D" rel="nofollow">David Bamman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Klein,+L+F" rel="nofollow">Lauren F. Klein</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dodge,+J" rel="nofollow">Jesse Dodge</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters, by Li Lucy and 6 other authors</div> <a class="mobile-submission-download" href="/pdf/2401.06408">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2401.06408v3">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Large language models' (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation. However, decisions around what data is retained or removed during this initial stage are under-scrutinized. In our work, we ground web text, which is a popular pretraining data source, to its social and geographic contexts. We create a new dataset of 10.3 million self-descriptions of website creators, and extract information about who they are and where they are from: their topical interests, social roles, and geographic affiliations. Then, we conduct the first study investigating how ten "quality" and English language identification (langID) filters affect webpages that vary along these social dimensions. Our experiments illuminate a range of implicit preferences in data curation: we show that some quality classifiers act like topical domain filters, and langID can overlook English content from some regions of the world. Overall, we hope that our work will encourage a new line of research on pretraining data curation practices and its social implications. </blockquote>  <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">28 pages, 13 figures. Association for Computational Linguistics (ACL) 2024</td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Computation and Language (cs.CL)</span></td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2401.06408">arXiv:2401.06408</a> [cs.CL]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2401.06408v3">arXiv:2401.06408v3</a> [cs.CL]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2401.06408" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2401.06408</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 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[2401.06408] AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters