Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare

TL;DR

This paper presents a crowdsourcing framework with multi-stage quality control to improve data quality for training healthcare large language models, demonstrating significant enhancements in model recall and data quality in resource-limited settings.

Contribution

It introduces a comprehensive crowdsourcing approach with real-time and post-data collection quality controls tailored for healthcare data, improving LLM training effectiveness.

Findings

Real-time quality control improves data quality by 19%.

Crowdsourced data enhances recall in Bio-BERT models.

Quality control impacts precision and recall balance.

Abstract

Large Language Models (LLMs) have demonstrated immense potential in artificial intelligence across various domains, including healthcare. However, their efficacy is hindered by the need for high-quality labeled data, which is often expensive and time-consuming to create, particularly in low-resource domains like healthcare. To address these challenges, we propose a crowdsourcing (CS) framework enriched with quality control measures at the pre-, real-time-, and post-data gathering stages. Our study evaluated the effectiveness of enhancing data quality through its impact on LLMs (Bio-BERT) for predicting autism-related symptoms. The results show that real-time quality control improves data quality by 19 percent compared to pre-quality control. Fine-tuning Bio-BERT using crowdsourced data generally increased recall compared to the Bio-BERT baseline but lowered precision. Our findings highlighted the potential of crowdsourcing and quality control in resource-constrained environments and offered insights into optimizing healthcare LLMs for informed decision-making and improved patient care.