Challenge: Recent work shows that pre-trained language models perform poorly on challenging datasets where spurious correlations do not hold.
Approach: They propose to use multi-task learning to improve generalization from minority examples . they propose to combine MTL with auxiliary tasks to improve performance .
Outcome: The proposed model generalizes from minority examples without hurting in-distribution performance.

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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.
Outcome: The proposed model can predict class significantly better when only ‘stop’ words are available at the input stage, but can degrade the ability of the system to generalize well to out-of-domain data.
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models (2022.findings-naacl)

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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.
Outcome: The proposed method can identify spurious correlations in NLP models at scale and mitigate these leads to more robust models in multiple applications.
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers (2022.findings-emnlp)

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Challenge: Existing methods to reduce model's reliance on bias features ignore the learnability of these features.
Approach: They propose to reduce models' reliance on bias features by first training models with fixed low-capacity models which ignore the learnability of the bias features.
Outcome: The proposed models can perform better on out-of-distribution datasets than baseline models with a more sophisticated model design.
Stubborn Lexical Bias in Data and Models (2023.findings-acl)

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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 .
Approach: They propose a method to reduce spurious correlations in training data by reweighting it using a large pool of extracted features.
Outcome: The proposed method reduces spurious correlations in training data, but still finds strong evidence of bias in trained models.
Increasing Robustness to Spurious Correlations using Forgettable Examples (2021.eacl-main)

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Challenge: Neural NLP models often exploit spurious correlations to perform tasks. minority examples have been shown to increase the out-of-distribution generalization of pre-trained language models.
Approach: They propose to use example forgetting to find minority examples without prior knowledge of spurious correlations in the dataset.
Outcome: The proposed approach improves out-of-distribution generalization on minorities . it shows that minority examples are more robust on challenging datasets .
Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)

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Challenge: Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts.
Approach: They revisit the experiments conducted by Gordon and Van Durme (2013) . they find that pre-trained language models overestimate the very rare .
Outcome: The proposed approach overestimates the rare at the expense of the rare, while minimizing reporting bias.
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.
Approach: They propose to generate a debiased version of a dataset and replace it with training data to train a model that is generalised to different task distributions.
Outcome: The proposed method outperforms or performs comparable to state-of-the-art debiasing strategies on a large suite of debiased, out-of distribution, and adversarial test sets.
Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)

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Challenge: Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem.
Approach: They propose to incorporate some of these measures into training objectives to enhance distributional robustness of LLMs.
Outcome: The proposed models outperform human models on complex tasks and outperformed other models on deep networks.
Assessing Combinational Generalization of Language Models in Biased Scenarios (2022.aacl-short)

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Challenge: Existing work focuses on assessing in-domain knowledge, but shedding light on what pre-trained Language Models learn is important.
Approach: They propose a method to assess a PLM's generalization capacity in biased scenarios by combining component combinations where it could be easy for the PLMs to learn shortcuts from the training corpus.
Outcome: The proposed model can overcome distribution shifts in the training corpus and with sufficient data.
End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)

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Challenge: Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task.
Approach: They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets.
Outcome: The proposed methods improve robustness in all settings and transfer better to out-of-domain datasets.

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