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      <datestamp>2026-10-14T12:02:23Z</datestamp>
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        <dc:identifier>http://dx.doi.org/10.59704/a3baf260ef374567</dc:identifier>
        <dc:identifier>https://verfassungsblog.de/the-currently-uneven-intelligence-of-ai-in-comparative-constitutional-law/</dc:identifier>
        <dc:title>The (Currently) Uneven Intelligence of AI in Comparative Constitutional Law - From Years of Research to a Single Prompt?</dc:title>
        <dc:creator>Chilton, Adam</dc:creator>
        <dc:creator>Langlois, Arthur</dc:creator>
        <dc:creator>Dittmer, Jan-Ulrich</dc:creator>
        <dc:creator>Versteeg, Mila</dc:creator>
        <dc:language>eng</dc:language>
        <dc:date>2026-10-14</dc:date>
        <dc:type>electronic resource</dc:type>
        <dc:format>text/html</dc:format>
        <dc:subject>ddc:342</dc:subject>
        <dc:subject>AI</dc:subject>
        <dc:subject>Artificial Intelligence</dc:subject>
        <dc:subject>Comparative Constitutionalism</dc:subject>
        <dc:subject>Research</dc:subject>
        <dc:publisher>Verfassungsblog</dc:publisher>
        <dc:relation>Verfassungsblog--2366-7044</dc:relation>
        <dc:rights>CC BY-SA 4.0</dc:rights>
        <dc:description>Our results suggest that LLMs’ usefulness for comparative constitutional data collection depends on what researchers ask them to code. They may be able to produce datasets that simply code constitutional facts, but current versions of LLMs do not perform well when asked to create datasets that require interpreting abstract constitutional concepts or reasoning about law. However, since the models make errors in systematic ways, researchers should always be cautious about treating LLM-generated data as interchangeable with expert-coded data.</dc:description>
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