TurQUaz at CheckThat! 2024: A Hybrid Approach of Fine-Tuning and In-Context Learning for Check-Worthiness Estimation

Abstract

This paper presents our participation in the CLEF2024 CheckThat! Lab's Task-1 which focuses on determining whether passages from tweets or transcriptions are check-worthy. Task 1 covers three languages including English, Arabic, and Dutch. We propose utilizing several different instruct-tuned large language models (LLM) and aggregating their results for the Dutch dataset. In English and Arabic datasets, in addition to LLMs, we also use a fine-tuned XLM-R classifier. Our proposed method is ranked first in the Dutch dataset, fourth in the Arabic dataset, and eleventh in the English dataset. © 2024 Copyright for this paper by its authors.