De-Noising Document Classification Benchmarks via Prompt-based Rank Pruning: A Case Study
Year: 2024
Type: conference-paper
Abstract: This is a dataset of fan fiction works, labeled with any of 4 corresponding triggers warnings and judgements about the reliability of that label. Please finde details in the corresponding publication: https://webis.de/publications.html#wiegmann_2024c Please use the following citation key: @InProceedings{wiegmann:2024c, address = {Berlin Heidelberg New York}, author = {Matti Wiegmann and Benno Stein and Martin Potthast}, booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 15th International Conference of the CLEF Association (CLEF 2024)}, editor = {Lorraine Goeuriot and Philippe Mulhem and Georges Qu{\'e}not and Didier Schwab and Giorgio Maria Di Nunzio and Laure Soulier and Petra Galuscakova and Alba Garcia Seco Herrera and Guglielmo Faggioli and Nicola Ferro}, month = sep, publisher = {Springer}, series = {Lecture Notes in Computer Science}, site = {Grenoble, France}, title = {{De-Noising Document Classification Benchmarks via Prompt-based Rank Pruning: A Case Study}}, year = 2024}
Authors: Wiegmann, Matti, Potthast, Martin, Stein, Benno
Institution: Bauhaus-Universität Weimar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949269179
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