Selecting The Most Informative Tokens in Natural Language Autoencoders
It is shown that useful explanations can extend beyond the model a verbalizer was trained to describe and show that pretrained verbalizers recover words that models have learned to conceal through fine-tuning, without additional verbalizer training.