  This paper describes IAI group's participation for automated check-worthiness
estimation for claims, within the framework of the 2024 CheckThat! Lab "Task 1:
Check-Worthiness Estimation". The task involves the automated detection of
check-worthy claims in English, Dutch, and Arabic political debates and Twitter
data. We utilized various pre-trained generative decoder and encoder
transformer models, employing methods such as few-shot chain-of-thought
reasoning, fine-tuning, data augmentation, and transfer learning from one
language to another. Despite variable success in terms of performance, our
models achieved notable placements on the organizer's leaderboard: ninth-best
in English, third-best in Dutch, and the top placement in Arabic, utilizing
multilingual datasets for enhancing the generalizability of check-worthiness
detection. Despite a significant drop in performance on the unlabeled test
dataset compared to the development test dataset, our findings contribute to
the ongoing efforts in claim detection research, highlighting the challenges
and potential of language-specific adaptations in claim verification systems.
