Beam search is a way to increase the accuracy of autoregressive models . Beam search can also provide users with multiple candidate answers to choose from, similar to the top-n in image classification. However, one issue with beam search is that it will favour shorter sequences as a sub-sequence will always have a higher probability than its parent sequence . There are multiple methods to solve this issue. Some are mentioned in and . In this paper, we use a simple method mentioned in where we multiply 1 L α to the log probability where L is the length of the sequence. This will make the log probability of a long sequence higher (since the log probability is negative). It can also be seen as taking the mean of the logprob of each token when α = 1. The re-scored logprob is shown in Equation 2 where P (y t |y 1 . . . y t-1 , e) is the probability of output y t at t-th token given previous tokens y 1 . . . y t-1 and image embedding e.