State the main arguments that the abstract makes
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 their nearest neighbor discrete projections: We 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), while 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 various parameters. For instance, for larger model sizes we observe higher waywardness, i.e, we can find prompts that more closely map to any arbitrary text with a smaller drop in accuracy. These findings have important implications relating to the difficulty of faithfully interpreting continuous prompts and their generalization across models and tasks, providing guidance for future progress in prompting language models.
The main argument of this paper is that discretizing continuous prompts for natural language processing tasks has its drawbacks, as evidenced by "wayward" behavior that can be observed. Such behavior is especially prevalent when larger model sizes are used. This in turn has important implications for faithfully interpreting continuous prompts and generalizing them across models and tasks, and necessitates potential changes to the approach used.
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### Instruction:
State the main arguments that the abstract makes

### Response:
The main argument of this paper is that discretizing continuous prompts for natural language processing tasks has its drawbacks, as evidenced by "wayward" behavior that can be observed. Such behavior is especially prevalent when larger model sizes are used. This in turn has important implications for faithfully interpreting continuous prompts and generalizing them across models and tasks, and necessitates potential changes to the approach used.