Ishibashi, Y, Bollegala, D 
ORCID: 0000-0003-4476-7003, Sudoh, K and Nakamura, S
(2023)
Evaluating the Robustness of Discrete Prompts.
In: European Chapter of the Association for Computational Linguistics (EACL-2023), 2023-5-3 - 2023-5-5, Croatia.
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Abstract
Discrete prompts have been used for finetuning 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.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 06 Feb 2023 16:16 |
| Last Modified: | 11 Jun 2023 03:16 |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3168209 |
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