The model's calibration is the metric determining if the model's output accuracy is aligned with its uncertify. The calibration metric of a model is important when it needs to make important decisions such as resume screaming and malware detection . The calibration is important for our sequence-generating task for another reason: we will select the output with the lowest uncertainty (highest logprob) as the final output. We evaluated our model's calibration using a method similar to , we randomly sample in each sample, we randomly select 30 molecules with replacement and then randomly choose images for each molecule. We calculate the mean accuracy for each sample and the mean perplexity (logprob) for each sample and plot them in Figure . Because the sequence probability (perplexity) is the product of the probabilities of each token, the value is usually not normalized in [0,1] range. Thus, it did not use the commonly used metric expected calibration error (ECE) like in . Even in , they did not evaluate the calibration for generative tasks. Instead, we used the r 2 value to evaluate the calibration of the model. This is partially inspired by the r 2 values used in evaluating the quality of the calibration curve in analytical chemistry.