PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning (2024.acl-long)
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| Challenge: | Recent research shows that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. |
| Approach: | They propose to use contrastive learning to promote global feature alignment and learning counterfactual clues to improve model performance. |
| Outcome: | The proposed method outperforms the state-of-the-art on out-of distribution (OOD) datasets. |
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| Challenge: | Pretrained language models tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. |
| Approach: | They propose to use counterfactually-augmented data (CAD) to identify robust features that are invariant under distribution shift to train models for OOD generalization. |
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How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? (2021.emnlp-main)
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| Challenge: | Recent studies have shown that models trained on CAD can learn cues in the dataset which are spuriously correlated with the construct. |
| Approach: | They focus on sentiment, sexism, and hate speech as social constructs to investigate their effects on model performance. |
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Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection (2022.naacl-main)
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| Challenge: | sexism and hate speech detection models may be over-relying on core features . construct-driven CAD may induce models to ignore context in which core features are used . |
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People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection (2023.emnlp-main)
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| Challenge: | Past work has shown that counterfactually augmented data (CADs) can improve models' performance on out-of-domain tests. |
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AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning (2022.findings-emnlp)
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| Challenge: | Existing methods for generating counterfactuals rely on human efforts or task-specific designs. |
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| Challenge: | In many natural language processing tasks, model training often leads to spurious correlations . shortcuts allow models to rely on irrelevant patterns in the data, leading to biased predictions. |
| Approach: | They propose a method to generate structure-aware positive and negative sentences using tagging. |
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Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models (2023.acl-long)
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| Challenge: | Existing methods for classification are overly confident on unseen examples . despite recent advances in NLP, some categories of distribution shift still pose serious challenges. |
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CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration (2024.eacl-long)
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| Challenge: | Large language models (LLMs) have shown remarkable generalization capabilities, performing well on various tasks such as question answering (QA), complex reasoning, and code generation. |
| Approach: | They propose to augment training data of smaller language models with automatically generated counterfactuals (CF) instances to improve out-of-domain (OOD) performance of SLMs in extractive question answering setup. |
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Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for finding meaningful counterfactuals rely on human annotation or implicit label invariance . a small amount of human-annotated counterf actual data can generate a robust dataset with learned labels. |
| Approach: | They propose a framework that generates counterfactuals by actively sampling from regions of uncertainty and automatically labeling them with a learned auxiliary classifier. |
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CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation (2022.findings-emnlp)
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| Challenge: | Prior work on counterfactual data augmentation only considered restricted classes of perturbations, limiting their effectiveness. |
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