However, the core difficulty of finding concrete explanations for the poor TroFi results and the excellent concatenated dataset and PoFo results is the interpretability limitation of LLMs due to their “black-box” nature. Contrary to statistical machine learning approaches, no information is provided about which features contribute most to the classification task during LLM fine-tuning, making the reasoning behind the model’s classification result not entirely comprehensible (Dobson 2023, 431). This is especially concerning for the Digital Humanities, where understanding the domain is just as important as raw performance. Ablation techniques and visualisations of attention weights could help understanding how model output was created.