Using large language models for extracting and pre-annotating texts on mental health from noisy data in a low-resource language.

ABSTRACT: Recent advancements in large language models (LLMs) have opened new possibilities for developing conversational agents (CAs) in various subfields of mental healthcare. However, this progress is hindered by limited access to high-quality training data, often due to privacy concerns and high annotation costs for low-resource languages. A potential solution is to create human-AI annotation systems that utilize extensive public domain user-to-user and user-to-professional discussions on social media. These discussions, however, are extremely noisy, necessitating the adaptation of LLMs for fully automatic cleaning and pre-classification to reduce human annotation effort. To date, research on LLM-based annotation in the mental health domain is extremely scarce. In this article, we explore the po

SUBMITTER: Koltcov S 

PROVIDER: S-EPMC11623104 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature