Papers with generative fine-tuning
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference. |
| Approach: | They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning. |
| Outcome: | The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios. |
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. |