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Although we can learn many things from Table 1, I have a concern on the experiments in the aspect of the limited evaluation. Many researchers have given attention to the powerful generation capability of Transformer decoders. But, this paper only measured the perplexities, the probabilistic results of language models. I think it seems to be not sufficient to note that there is no performance degradation on generation tasks. How about zero/few-shot evaluation results? With various kinds of tasks, the contribution of this paper would be strengthened.
I have also another concern on the acceleration of this method, the vector-wise quantization and decomposition method. There is a short explanation about acceleration in Appendix A and Table 3. But I think it is not sufficient to prove there is no acceleration overhead. There is no description on the environmental setup of inference measurement. I think this paper should include some arguments about this issue in detail.
To support their assertions, I think this paper can be refined in two aspects: 1) additional experimental results on performance of generation models, and 2) additional arguments in the acceleration environment and implementation.
1) additional experimental results on performance of generation models, and