| Challenge: | Existing methods to identify spurious instances require either annotations generated by each individual annotator or both task-specific and instance-type annotations. |
| Approach: | They propose an approach that discriminates instances based on their "difficulty to learn" they use queueing theory and psychology of learning to improve annotations . |
| Outcome: | The proposed methods outperform state-of-the-art baselines and have a MAP of 0.85 and 0.22 in identifying spurious instances in synthetic and carefully-crowdsourced real-world datasets respectively. |
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| Challenge: | Existing work identifies task-specific shortcuts via human priors or error analyses, which requires extensive expertise and efforts. |
| Approach: | They propose to automatically identify spurious correlations in NLP models at scale by using existing interpretability methods to extract tokens that significantly affect model’s decision process. |
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Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets (2022.acl-long)
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| Challenge: | Natural language processing models exploit spurious correlations between features and labels in datasets to perform well only within the distributions they are trained on. |
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Competency Problems: On Finding and Removing Artifacts in Language Data (2021.emnlp-main)
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Matt Gardner, William Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh, Noah A. Smith
| Challenge: | Recent work in NLP has documented dataset artifacts, bias, and spurious correlations . how to tell which features have spurious instead of legitimate correlations is typically left unspecified . |
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Identifying Spurious Correlations for Robust Text Classification (2020.findings-emnlp)
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| Challenge: | Text classifiers often rely on spurious correlations to predict positive reviews . term Spielberg does not cause the review to be positive, so it does not affect the classification accuracy. |
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| Challenge: | Recent work has focused on spurious correlations between features and labels in training data . but, we find strong evidence of corresponding bias in the trained models . |
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Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis (2024.findings-eacl)
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| Challenge: | Recent research has revealed that machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances. |
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Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates (2021.findings-emnlp)
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| Challenge: | Existing approaches to interpret black-box models to learn spurious correlations are not well understood. |
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Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers (2023.acl-long)
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| Challenge: | toxicity and IMDB review datasets show that pre-trained NLP classifiers learn spurious correlations between input features and label . |
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Detecting Label Errors by Using Pre-Trained Language Models (2022.emnlp-main)
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| Challenge: | Existing methods for label error detection focus on label errors in training data. |
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Analyzing Biases to Spurious Correlations in Text Classification Tasks (2022.aacl-short)
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| Challenge: | Often these systems exceed human performance, but there is a caveat: standard benchmarks often assume that training and evaluation data are drawn independently and identically from the same underlying distribution. |
| Approach: | They propose to exploit spurious correlations in training data to exploit these correlations . they show that even when only ‘stop’ words are available, it is possible to predict the class significantly better than random. |
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