The methods of the Graph Project mirror the progression of methods in the NLP tradition as the authors experimented with rule-based and statistical approaches (Kesarwani et al. 2017). The F1 scores for their rule-based and machine learning models were 0.669 for PoFo, 0.827 for TroFi, 0.779 for MOH and 0.781 for the concatenated dataset. Tanasescu, Kesarwani, and Inkpen (2018) used deep learning with convolutional neural networks on the concatenated dataset and reached an F1 score of 0.833. Since fine-tuning LLMs proved to be a very successful approach in recent NLP research (e.g., a F1 score of 0.944 on TroFi by Ma et al. 2021), this paper tests whether this method will improve the MD performance on the Graph project’s datasets.