Evaluating the Robustness of Discrete Prompts

TL;DR

This paper systematically evaluates the robustness of automatically learned discrete prompts in NLP, revealing their sensitivity to certain perturbations and poor cross-dataset generalization, which highlights areas for future improvement.

Contribution

It provides the first comprehensive analysis of the robustness of discrete prompts, identifying their vulnerabilities and limitations across different perturbations and datasets.

Findings

Discrete prompts are sensitive to token shuffling and deletion.

They show robustness to input perturbations in NLI tasks.

Poor cross-dataset generalization of learned prompts.

Abstract

Discrete prompts have been used for fine-tuning Pre-trained Language Models for diverse NLP tasks. In particular, automatic methods that generate discrete prompts from a small set of training instances have reported superior performance. However, a closer look at the learnt prompts reveals that they contain noisy and counter-intuitive lexical constructs that would not be encountered in manually-written prompts. This raises an important yet understudied question regarding the robustness of automatically learnt discrete prompts when used in downstream tasks. To address this question, we conduct a systematic study of the robustness of discrete prompts by applying carefully designed perturbations into an application using AutoPrompt and then measure their performance in two Natural Language Inference (NLI) datasets. Our experimental results show that although the discrete prompt-based method remains relatively robust against perturbations to NLI inputs, they are highly sensitive to other types of perturbations such as shuffling and deletion of prompt tokens. Moreover, they generalize poorly across different NLI datasets. We hope our findings will inspire future work on robust discrete prompt learning.