Summary of Diagnosing Medical Datasets with Training Dynamics, by Laura Wenderoth
Diagnosing Medical Datasets with Training Dynamics
by Laura Wenderoth
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 study examines the possibility of using training dynamics as an automated alternative to human annotation for evaluating training data quality, leveraging Data Maps to classify data points into categories like easy-to-learn, hard-to-learn, and ambiguous. The findings suggest that difficult-to-learn examples often contain errors, while ambiguous cases significantly impact model training. To validate these results, the experiment was replicated using a challenging medical question answering dataset, requiring both text comprehension and detailed medical knowledge acquisition. A comprehensive evaluation assessed the framework’s feasibility and transferability to the medical domain, revealing it is unsuitable for addressing unique challenges in medical question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to use training data better by automating human annotation. It uses a special tool called Data Maps to sort training data into groups like easy or hard to learn. The researchers found that hard-to-learn examples often have errors, and unclear cases affect how well models train. To check their results, they tried it with a difficult medical question answering dataset, which requires understanding medical details. They tested the tool’s usefulness for medical questions and found it doesn’t work well for this specific task. |
Keywords
» Artificial intelligence » Question answering » Transferability
