Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

Albert Webson, Ellie Pavlick


Abstract
Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. In this study, we experiment with over 30 prompts manually written for natural language inference (NLI). We find that models can learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively “good” prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on hundreds of prompts (Sanh et al., 2021). That is, instruction-tuned models often produce good predictions with irrelevant and misleading prompts even at zero shots. In sum, notwithstanding prompt-based models’ impressive improvement, we find evidence of serious limitations that question the degree to which such improvement is derived from models understanding task instructions in ways analogous to humans’ use of task instructions.
Anthology ID:
2022.naacl-main.167
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2300–2344
Language:
URL:
https://aclanthology.org/2022.naacl-main.167
DOI:
10.18653/v1/2022.naacl-main.167
Bibkey:
Cite (ACL):
Albert Webson and Ellie Pavlick. 2022. Do Prompt-Based Models Really Understand the Meaning of Their Prompts?. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2300–2344, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Do Prompt-Based Models Really Understand the Meaning of Their Prompts? (Webson & Pavlick, NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.167.pdf
Code
 awebson/prompt_semantics
Data
ANLISuperGLUE