Prompt-based learning and its application in mental health

Abstract

With the rise of large language models (LLMs), prompt-based learning has become a widely studied and essential topic in the field of Natural Language Processing (NLP). Due to their impressive performance in zero-shot and few-shot settings, prompting offers a strong opportunity for low-resource domains such as mental health. Mental health is a prevalent global concern, with increasing numbers of users sharing their experiences on social media. However, data from these platforms is often complex, and manual annotation at scale is both time-consuming and costly.

This thesis investigates the central hypothesis that task-specific and well-designed prompt strategies are essential for effectively adapting LLMs to low-resource domains, with a particular focus on mental health applications.

This study systematically examines the role of prompt position across tasks and prompting methods, revealing that position significantly affects model performance and is often overlooked in prior work. A domain-specific prompting method is then explored to extract and summarise evidence of suicidal ideation with minimal supervision. Finally, a novel LLM-based framework is developed for multi-label life event annotation in the mental health domain, incorporating tailored prompting techniques and prompt position self-ordering to enable effective automatic annotation.

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Identifiers

ORCID for Junyu Mao: orcid.org/0000-0001-8171-5216

ORCID for Stuart Middleton: orcid.org/0000-0001-8305-8176

ORCID for Mahesan Niranjan: orcid.org/0000-0001-7021-140X

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