How do you address the challenge of generating repetitive or generic translations with Transformer models?

Generating repetitive or generic translations is a common challenge with Transformer models, and several techniques can be used to address it. One approach is to use techniques that encourage diversity in the generated output, such as temperature sampling or top-k sampling. Temperature sampling involves adjusting the softmax distribution by a temperature parameter, which controls the randomness of the sampling process. A higher temperature makes the distribution more uniform, leading to more diverse outputs. Top-k sampling involves selecting the top k most likely words from the softmax distribution and then sampling from these words. This limits the number of possible words that can be generated, but it als....