| Challenge: | Existing methods for OOD detection and ID classification tasks require massive amounts of ID labeled data and no OOD labeles. |
| Approach: | They propose to use OOD-resistant Prototypical Network to detect OOD cases with limited in-domain (ID) training data to solve this task. |
| Outcome: | The proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task while maintaining a competitive performance on ID classification task. |
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Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
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Out-of-domain Detection based on Generative Adversarial Network (D18-1)
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| Challenge: | Existing methods for out-of-domain (OOD) detection require huge effort to collect OOD sentences. |
| Approach: | They propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. |
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Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection (2024.lrec-main)
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Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
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Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks (2023.acl-srw)
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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. |
| Approach: | They evaluated eight methods that are easily integrable into existing NLP systems and require no additional OOD data or model modifications. |
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Assessing Out-of-Domain Language Model Performance from Few Examples (2023.eacl-main)
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| Challenge: | Pretrained language models exhibit impressive generalization capabilities, but behave unpredictably under certain domain shifts. |
| Approach: | They propose to incorporate attributions into a few-shot model predicting out-of-domain (OOD) performance task to find out if models agree with pathological heuristics that may indicate worse generalization capabilities. |
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Improving Unsupervised Out-of-domain detection through Pseudo Labeling and Learning (2023.findings-eacl)
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| Challenge: | Unsupervised OOD detection is a task aimed at discriminating whether given samples are from the in-domain (IND) . previous studies adopted the one-class classification approach, assuming that the training samples come from a single domain. |
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Adversarial Self-Supervised Learning for Out-of-Domain Detection (2021.naacl-main)
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| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are unsupervised and require extensive labeled data. |
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Out-of-Distribution Detection via LLM-Guided Outlier Generation for Text-attributed Graph (2025.findings-acl)
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| Challenge: | Text-Attributed Graphs (TAGs) are widely used in the real world. |
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Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning (2021.acl-short)
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| Challenge: | Existing methods of OOD detection only focus on whether a sample is correctly classified . lack of real OOD examples leads to poor prior knowledge about these unknown intents . |
| Approach: | They propose a supervised contrastive learning objective to minimize intra-class variance . they employ an adversarial augmentation mechanism to obtain pseudo diverse views . |
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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. |
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