| Challenge: | Pretrained Transformers are more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. |
| Approach: | They construct a new robustness benchmark with real distribution shifts to measure out-of-distribution generalization for seven NLP datasets and compare them to previous models. |
| Outcome: | The proposed model generalizations for seven datasets show that pretrained Transformers are significantly less effective at detecting anomalous or OOD examples, while many previous models are often worse than chance. |
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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
| Approach: | They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
| Outcome: | The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
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. |
| Outcome: | The proposed method outperforms baselines in the real-world and achieves near-perfect OOD detection performance. |
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective (2023.findings-acl)
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Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang
| Challenge: | Pre-trained language models (PLMs) have improved generalization performance but the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks. |
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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. |
| Approach: | They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning. |
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Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding (2023.eacl-main)
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| Challenge: | Recent studies have focused on compressing pre-trained language models (PLMs) however, few studies have examined the impact of compression on generalizability and robustness of compressed models for out-of-distribution data. |
| Approach: | They propose to use knowledge distillation and pruning to reduce model generalization and generalization on out-of-distribution data. |
| Outcome: | The proposed compression techniques overfit on shortcut samples and generalize poorly on hard ones. |
BERT Busters: Outlier Dimensions that Disrupt Transformers (2021.findings-acl)
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| Challenge: | Existing studies show that pre-trained Transformers are remarkably robust to pruning. |
| Approach: | They show that pre-trained Transformer encoders are surprisingly fragile to pruning . they show that disabling them significantly degrades both the MLM loss and the downstream task performance. |
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Are Sample-Efficient NLP Models More Robust? (2023.acl-short)
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| Challenge: | Recent work in image classification and extractive question answering have observed that pre-trained models trained on less in-distribution data have better out-of-distortion performance. |
| Approach: | They conduct a large empirical study to investigate the relationship between sample efficiency and robustness. |
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Calibration of Pre-trained Transformers (2020.emnlp-main)
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| Challenge: | Pre-trained Transformers dominate benchmark tasks but use a large number of self-attention heads across many layers in a way that is difficult to unpack. |
| Approach: | They analyze pre-trained Transformer models' posterior probabilities to determine whether they are calibrated for three tasks: natural language inference, paraphrase detection, and commonsense reasoning. |
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On Robustness of Finetuned Transformer-based NLP Models (2023.findings-emnlp)
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| Challenge: | Pretrained Transformer-based language models have been finetuned for a large number of tasks. |
| Approach: | They characterize changes between pretrained and finetuned models with CKA and STIR metrics. |
| Outcome: | The proposed models are more robust to perturbations than BERT and T5 on classification tasks and generation tasks. |
RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data (2023.emnlp-main)
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| Challenge: | Out-of-distribution (OOD) detection is a widely covered topic in classification tasks, but most methods rely on hidden features output by the encoder. |
| Approach: | They propose to leverage soft-probabilities in a black-box framework to detect OOD . they propose to use a more operational evaluation setting to enable OOD detection . |
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