Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs (2025.emnlp-main)
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| Challenge: | specialized large language models (LLMs) are capable of generating coherent, contextually relevant and often human-level language, but they remain vulnerable to errors when confronted with data that falls outside the scope of their domain. |
| Approach: | They propose an inference-time out-of-domain (OOD) detection algorithm that leverages the Inductive Conformal Anomaly Detection framework and a new non-conformity measure based on the model’s dropout tolerance. |
| Outcome: | The proposed method improves detection while maintaining theoretical false alarm bounds from ICAD while maintaining false alarms bounds. |
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