Position-based Prompting for Health Outcome Generation

Micheal Abaho, Danushka Bollegala, Paula Williamson, Susanna Dodd


Abstract
Probing factual knowledge in Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomenon has been effective, especially when these LMs are fine-tuned towards not just data, but also to the style or linguistic pattern of the prompts themselves. We observe that satisfying a particular linguistic pattern in prompts is an unsustainable, time-consuming constraint in the probing task, especially because they are often manually designed and the range of possible prompt template patterns can vary depending on the prompting task. To alleviate this constraint, we propose using a position-attention mechanism to capture positional information of each word in a prompt relative to the mask to be filled, hence avoiding the need to re-construct prompts when the prompts’ linguistic pattern changes. Using our approach, we demonstrate the ability of eliciting answers (in a case study on health outcome generation) to not only common prompt templates like Cloze and Prefix but also rare ones too, such as Postfix and Mixed patterns whose masks are respectively at the start and in multiple random places of the prompt. More so, using various biomedical PLMs, our approach consistently outperforms a baseline in which the default PLMs representation is used to predict masked tokens.
Anthology ID:
2022.bionlp-1.3
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–36
Language:
URL:
https://aclanthology.org/2022.bionlp-1.3
DOI:
10.18653/v1/2022.bionlp-1.3
Bibkey:
Cite (ACL):
Micheal Abaho, Danushka Bollegala, Paula Williamson, and Susanna Dodd. 2022. Position-based Prompting for Health Outcome Generation. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 26–36, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Position-based Prompting for Health Outcome Generation (Abaho et al., BioNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bionlp-1.3.pdf
Video:
 https://aclanthology.org/2022.bionlp-1.3.mp4
Data
EBM-NLP