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Topic Classification of German Web Data

We explore the application of large language models for topic classification within a low-resource German web environment, leveraging a dataset comprising millions of scraped webpages aimed at evaluating policy impacts.

Poster: alt text

Citation:

@inproceedings{schelb-etal-2024-assessing,
    title = "Assessing In-context Learning and Fine-tuning for Topic Classification of {G}erman Web Data",
    author = "Schelb, Julian  and
      Spitz, Andreas  and
      Ulloa, Roberto",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-srw.22",
    pages = "238--252",
}