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. |
| Approach: | They propose a framework that leverages latent categorical information to improve representation learning for textual OOD detection. |
| Outcome: | The proposed framework significantly outperforms baseline models on three datasets. |
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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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Unsupervised Out-of-Domain Detection via Pre-trained Transformers (2021.acl-long)
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| Challenge: | Prior work on out-of-domain detection requires in-domain task labels and is limited to supervised classification scenarios. |
| Approach: | They propose a method to construct out-of-domain detectors efficiently using pre-trained transformers. |
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Contrastive Out-of-Distribution Detection for Pretrained Transformers (2021.emnlp-main)
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| Challenge: | Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution, but in real-world scenarios, out-of-distribution instances can cause semantic shift problems. |
| Approach: | They propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, and to use the Mahalanobis distance in the model's penultimate layer to detect OOD instances. |
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Semantic Role Labeling Guided Out-of-distribution Detection (2024.lrec-main)
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Jinan Zou, Maihao Guo, Yu Tian, Yuhao Lin, Haiyao Cao, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi
| Challenge: | Existing methods for identifying domain-shifted instances are prone to OOD and adversarial inputs. |
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VI-OOD: A Unified Framework of Representation Learning for Textual Out-of-distribution Detection (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is a crucial part of deep neural networks. |
| Approach: | They propose a variational inference framework which maximizes the likelihood of the joint distribution p(x, y) instead of p[y|x). |
| Outcome: | The proposed framework maximizes the likelihood of the joint distribution p(x, y) instead of p[y|x). |
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. |
| Approach: | They propose a self-supervised contrastive learning framework to model discriminative semantic features from unlabeled data. |
| Outcome: | The proposed framework outperforms baseline methods on two public benchmark datasets with a statistically significant margin. |
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
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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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Out-of-Domain Detection for Low-Resource Text Classification Tasks (D19-1)
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| 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. |
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APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection (2023.findings-emnlp)
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Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are hard to label . previous studies use labeled in-domain data to learn intent representations . |
| Approach: | They propose a prototypical pseudo-labeling method for few-shot OOD detection . they propose 'protoOOD' framework and adaptive pseudo-labeled method . |
| Outcome: | The proposed method is able to detect out-of-domain (OOD) intents from user queries. |
Estimating Soft Labels for Out-of-Domain Intent Detection (2022.emnlp-main)
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| Challenge: | Existing methods to detect out-of-dominance (OOD) intents are limited by the lack of OOD samples. |
| Approach: | They propose an adaptive soft pseudo labeling method that can estimate soft labels for pseudo OOD samples when training OOD detectors. |
| Outcome: | The proposed method outperforms competing methods on three benchmark datasets and consistently outperformed previous methods. |