To understand the relative importance of different spectral data types on model performance, we conducted an ablation study by omitting each spectral modality in turn during single-epoch fine-tuning iterations of the fully trained MMT model. We evaluated the impact on three key metrics: averaged correct SMILES sample probability, average greedy sampled Tanimoto similarity, and number of invalid molecules generated. The results, illustrated in Figure , reveal that 2D NMR data (HSQC and COSY) contribute most significantly to the model's performance. Omitting HSQC data led to the most substantial drops in correct SMILES probability (0.51 to 0.04) and Tanimoto similarity (0.82 to 0.43), while also resulting in the highest number of invalid molecules (44,847). COSY omission showed the second-largest impact, particularly evident in the notable increase of invalid molecules (38,798). It's important to note that the model's interpretation of spectral importance may differ from that of human spectroscopists. Each spectral embedding in our model includes molecular weight and formula information, enhancing its information content beyond what's visually apparent in the spectrum alone. This additional context influences the model's prioritization of different modalities. Interestingly, while 13 C NMR showed less impact on model performance, its time-consuming acquisition process in practice might make it a candidate for deprioritization in time-sensitive scenarios. Conversely, IR spectroscopy, despite showing minimal effect on performance in this study, offers rapid data collection, potentially making it valuable in practical applications where speed is crucial. Interestingly, while 2D NMR techniques (HSQC and COSY) showed the most significant impact on model performance, the time-consuming acquisition process of C NMR in practice might make it a candidate for deprioritization in time-sensitive scenarios, given its relatively lower impact on model performance. Conversely, IR spectroscopy, despite showing minimal effect on performance in this study, offers rapid data collection, potentially making it valuable in practical applications where speed is crucial. The substantial influence of 2D NMR data suggests that prioritizing HSQC and COSY spectra acquisition could significantly enhance structure elucidation accuracy, especially when balanced against time and resource constraints.