Position-based Prompting for Health Outcome Generation
This work addresses the inefficiency of manual prompt design for domain-specific tasks like health outcome generation, offering a more flexible approach, though it is incremental in improving existing prompting methods.
The paper tackled the problem of manually designing prompt templates for probing pre-trained language models by introducing a position-attention mechanism to capture word positions relative to masks, enabling handling of rare patterns like Postfix and Mixed in health outcome generation. It demonstrated consistent performance improvements over a baseline using default mask language model representations across various biomedical PLMs.
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.