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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-topicaldataset comprising texts of various sources and styles. Building on this, weperformed a benchmark analysis to determine the most general multilingual andmulti-topical claim detector.  We chose three state-of-the-art models in the check-worthy claim detectiontask and fine-tuned them. Furthermore, we selected three state-of-the-artextremely large language models without any fine-tuning. We made modificationsto the models to adapt them for multilingual settings and through extensiveexperimentation and evaluation. We assessed the performance of all the modelsin terms of accuracy, recall, and F1-score in in-domain and cross-domainscenarios. Our results demonstrate that despite the technological progress inthe area of natural language processing, the models fine-tuned for the task ofcheck-worthy claim detection still outperform the zero-shot approaches in across-domain settings.

Is it indeed bigger better? The comprehensive study of claim detection LMs applied for disinformation tackling