Papers by Jonas Zimmer
MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events (2026.acl-long)
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| Challenge: | Existing MLTC benchmarks are saturated and may be affected by training data contamination. |
| Approach: | They propose a machine learning benchmark based on medical device adverse event reports . they establish baselines across 20 encoder- and decoder-only models . |
| Outcome: | The proposed benchmarks show that small fine-tuned models achieve the strongest head-to-tail accuracy while maintaining competitive UQ. |