Position-based Prompting for Health Outcome Generation

TL;DR

This paper introduces a position-based prompting method that leverages position-attention mechanisms to improve health outcome generation by PLMs, reducing the need for manually designed prompts and handling diverse prompt patterns effectively.

Contribution

The paper proposes a novel position-attention approach for prompt design that captures positional information, enabling flexible prompt templates without manual re-engineering.

Findings

Outperforms baseline MLM representations in health outcome generation tasks.

Effectively handles diverse prompt patterns like Postfix and Mixed.

Demonstrates robustness across various biomedical PLMs.

Abstract

Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been effective especially when these LMs are fine-tuned towards not just data of a specific domain, 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 constraint that unnecessarily lengthens the probing task, especially because, they are often manually designed and the range of possible prompt template patterns can vary depending on the prompting objective and domain. We therefore explore an idea of 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 to rare prompt templates (in a case study on health outcome generation) such as Postfix and Mixed patterns whose missing information is 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 mask language model (MLM) representation is used to predict masked tokens.