Abstract

Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the feasibility of extracting a discrete (textual) interpretation of continuous prompts that is faithful to the problem they solve. In practice, we observe a “wayward” behavior between the task solved by continuous prompts and the nearest neighbor discrete projections of these prompts: One can find continuous prompts that solve a task while being projected to an arbitrary text (e.g., definition of a different or even a contradictory task) and simultaneously being within a very small (2%) margin of the best continuous prompt of the same size for the task. We provide intuitions behind this odd and surprising behavior, as well as extensive empirical analyses quantifying the effect of design choices. For instance, larger models exhibit higher waywardness, i.e, we can find prompts that more closely map to any arbitrary text with a smaller drop of accuracy. These findings have important implications relating to the difficulty of faithfully interpreting continuous prompts and their generalization across models and tas