Is it indeed bigger better? The comprehensive study of claim detection
  LMs applied for disinformation tackling
Martin Hyben Sebastian Kula Ivan Srba Robert Moro Jakub Simko
This study compares the performance of (1) fine-tuned models and (2)
extremely large language models on the task of check-worthy claim detection.
For the purpose of the comparison we composed a multilingual and multi-topical
dataset comprising texts of various sources and styles. Building on this, we
performed a benchmark analysis to determine the most general multilingual and
multi-topical claim detector.
  We chose three state-of-the-art models in the check-worthy claim detection
task and fine-tuned them. Furthermore, we selected three state-of-the-art
extremely large language models without any fine-tuning. We made modifications
to the models to adapt them for multilingual settings and through extensive
experimentation and evaluation. We assessed the performance of all the models
in terms of accuracy, recall, and F1-score in in-domain and cross-domain
scenarios. Our results demonstrate that despite the technological progress in
the area of natural language processing, the models fine-tuned for the task of
check-worthy claim detection still outperform the zero-shot approaches in a
cross-domain settings.