Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection (2023.findings-emnlp)
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| Challenge: | Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. |
| Approach: | They propose to apply world knowledge to enhance OOD detection performance through selective generation from large language models (LLMs) they propose to extract visual objects from each image to fully capitalize on the aforementioned world knowledge. |
| Outcome: | The proposed method outperforms the state-of-the-art on visual OOD detection on in-distribution (ID) samples. |
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| Challenge: | Text-Attributed Graphs (TAGs) are widely used in the real world. |
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| Challenge: | Out-of-Distribution (OOD) detection requires great generalization capability . |
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| Challenge: | Existing OOD methods often struggle with deliberately obfuscated, context-dependent, or superficially benign prompts. |
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| Challenge: | Current approaches to OOD detection in NLP are not yet sufficiently sensitive to capture all samples characterized by various types of distributional shifts. |
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| Challenge: | Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. |
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| Challenge: | Existing methods to improve robustness require changing the fine-tuning process or large-scale data augmentation, which are infeasible or cost prohibitive for closed-source models. |
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