Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate

1 code implementation • LREC 2022 • Lena Jurkschat, Gregor Wiedemann, Maximilian Heinrich, Mattes Ruckdeschel, Sunna Torge

We approach aspect-based argument mining as a supervised machine learning task to classify arguments into semantically coherent groups referring to the same defined aspect categories.

Argument Mining Few-Shot Learning

Tokenizer Choice For LLM Training: Negligible or Crucial?

no code implementations • 12 Oct 2023 • Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Schulze Buschhoff, Charvi Jain, Alexander Arno Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, Stefan Kesselheim, Nicolas Flores-Herr

The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.