In 2020, Shazeer et al. of Google implemented an improvement to multi-headed attention known as Talking Heads (TH) Attention. The modification is simple: a learned linear projection across the attention heads dimension is performed on the attention-logits vector (QK T in equation 1.)) before evaluating the softmax function. As the name suggests, this allows the attention heads to "talk" prior to generating the attention scores. Instead of making decisions as completely independent actors, there is discourse between the experts and the attention scores become a composite of the intermediate opinions of each individual head, instead of just the average of all final decisions. In their paper, Shazeer found that their modification provided meaningful improvements on transformers studying language comprehension and question answering tasks in the BERT and ALBERT 66 models. Talking heads attention has been notably implemented by Mao et al., who used to great success in GraphNovo, a tandem mass spectral (MS 2 ) peptide sequencing algorithm built on the original Graphormer architecture. We test the talking heads attention using the method of Shazeer et al. and a modified version of the GraphoNovo code found in reference 59.