Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare
<|reference_start|>Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare: 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.<|reference_end|>
@article{barai2024crowdsourcing,
    title={Crowdsourcing with Enhanced Data Quality Assurance: An Efficient
  Approach to Mitigate Resource Scarcity Challenges in Training Large Language
  Models for Healthcare},
    author={P. Barai, G. Leroy, P. Bisht, J. M. Rothman, S. Lee, J. Andrews, S. A.
  Rice, A. Ahmed},
    journal={arXiv preprint arXiv:2405.13030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13030},
    primaryClass={cs.CL cs.AI}
}