Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression (2024.findings-emnlp)
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| Challenge: | Existing methods for detecting out-of-distribution data are computationally complex and storage-intensive. |
| Approach: | They propose a soft clustering approach for OOD detection based on non-negative kernel regression . their approach greatly reduces computational and space complexities while retaining competitive performance. |
| Outcome: | The proposed approach outperforms existing methods by up to 4 AUROC points on four benchmarks while retaining competitive performance. |
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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. |
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Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks (2023.acl-srw)
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| Challenge: | Existing methods for supervised OOD detection require expensive manual annotation of in-distribution and OOD samples. |
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