Figure 2 presents the evaluation results of fine-tuning with the Transformers and SetFit methods on the MD task at sentence-level. All reported results are from evaluating the fine-tuned models on the test splits. At least one of the LLM fine-tuning approaches proposed in this paper outperformed the baseline on all datasets except for TroFi. For PoFo there was a significant performance increase of 12.41% with SetFit (F1 0.752) and 2.84% with Transformers (F1 0.688). The MOH dataset displayed an F1 score of 0.785 with the Transformers approach (0.77% increase). The SetFit approach demonstrated a significant improvement with an F1 score of 0.862 (10.37% increase) on the concatenated dataset. However, the TroFi dataset performed much better with the rule-based and statistical method, surpassing SetFit by 22.25%. As for the comparison of Transformers with SetFit, SetFit achieved better performance on PoFo, TroFi and the concatenated dataset, while Transformers gave better results on the MOH dataset. Overall, no method excelled across all datasets, although the LLM approaches generally performed better.