Summary of Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models For Healthcare, by P. Barai et al.
Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare
by P. Barai, G. Leroy, P. Bisht, J. M. Rothman, S. Lee, J. Andrews, S. A. Rice, A. Ahmed
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a crowdsourcing framework with quality control measures to address the challenges of creating high-quality labeled data for Large Language Models (LLMs) in low-resource domains like healthcare. The study evaluates the effectiveness of enhancing data quality on LLMs, specifically 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 increases recall but lowers precision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers created a new way to collect and improve labeled data for large language models in healthcare. This is important because collecting good data can be hard and expensive. They tested their method by fine-tuning a model called Bio-BERT to predict autism-related symptoms. The results showed that their method improved the quality of the data and helped the model make more accurate predictions. |
Keywords
» Artificial intelligence » Bert » Fine tuning » Precision » Recall
