Papers by Keon-Hee Park
CED: Comparing Embedding Differences for Detecting Out-of-Distribution and Hallucinated Text (2024.findings-emnlp)
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| Challenge: | Existing methods for detecting out-of-distribution (OOD) samples are limited due to their domain shift and computational limitations. |
| Approach: | They propose a training-free method to detect out-of-distribution (OOD) samples . they theoretically validate that specific auxiliary and oracle samples improve this distinction . |
| Outcome: | The proposed method improves the ability of pre-trained models to distinguish between ID and OOD samples in text classification and hallucination detection tasks. |