Papers by Ryan A. Cook
No Simple Answer to Data Complexity: An Examination of Instance-Level Complexity Metrics for Classification Tasks (2025.naacl-long)
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| Challenge: | Understanding data complexity at the instance level has become increasingly important in Natural Language Processing (NLP) and machine learning (ML). |
| Approach: | They empirically examine the relationship between instance-level complexity scores and metric selection for classification tasks. |
| Outcome: | The results show that storing training loss provides similar complexity rankings to other methods, but not demographic fairness, even in downstream predictions. |