The implications discussed in the paper apply *if* one were to try to project continuous prompts to discrete space using GPT-2's (or any other) embedding matrix, but, as far as I am aware, no one has attempted to interpret continuous prompts in this way. The paper could be strengthened by more explicitly discussing *why* we should be concerned with this particular method of prompt interpretation: 1. Do the authors think we should be especially concerned about nearest-neighbor, embedding matrix projections because that's how language model outputs and word2vec are calculated? This seems to be the case, but could be made more explicit. It's not clear why the use of an embedding matrix at the output layer of language models implies this would be a popular way of discretizing continuous prompts.
2. Is it because the particular discrete projection studied is the simplest that the authors believe it might be used in the future?
3. Do the authors think their hypothesis generalizes to other methods of discretely interpreting prompts? If so, that could also be made more explicit.
1. Space permitting, it may be beneficial to move the finding of Appendix B to the main paper as I found this interesting.
2. Line 246 is difficult to parse. Possibly split this into two sentences.
2. Is it because the particular discrete projection studied is the simplest that the authors believe it might be used in the future?