Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing (2022.coling-1)
Copied to clipboard
| Challenge: | Existing deep neural models rely on spurious correlations between prediction labels and input features, which in general suffer from robustness and generalization. |
| Approach: | They propose a feature decorrelation module to remove feature dependencies and reduce spurious correlations by learning a weight for each instance at the training phase. |
| Outcome: | The proposed method improves the robustness of the neural ANswer selection models from the sample and feature perspectives. |
Similar Papers
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to debiase samples with biased features obstructs the model in learning from non-biased parts of the samples. |
| Approach: | They propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective by using Random Fourier Features and weighted re-sampling to decorrelate dependencies between features. |
| Outcome: | The proposed method eliminates spurious correlations in a fine-grained manner from a feature space perspective. |
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models (2022.findings-naacl)
Copied to clipboard
| 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. |
When and Why Does Bias Mitigation Work? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Neural models exploit shallow surface features to perform language understanding tasks, rather than learning the deeper language understanding and reasoning skills that practitioners desire. |
| Approach: | They propose to use model debiasing techniques to pressure models away from spurious features and to use them to learn useful representations instead. |
| Outcome: | The proposed methods increase models' reliance on hidden biases instead of learning robust features that help them solve a task. |
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets (2022.acl-long)
Copied to clipboard
| 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. |
Stubborn Lexical Bias in Data and Models (2023.findings-acl)
Copied to clipboard
| 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. |
Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing work shows that Large Language Models (LLMs) are not robust to complex language understanding tasks due to reliance on spurious correlations of training datasets. |
| Approach: | They propose a method for measuring model reliance on spurious features by exploiting chosen biases on out-of-distribution (OOD) datasets. |
| Outcome: | The proposed method shows that the reported OOD gains of debiasing methods can't be explained by mitigated reliance on biased features, suggesting that biases are shared among different QA datasets. |
Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. |
| Approach: | They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels. |
| Outcome: | The proposed framework significantly improves out-of-distribution generalization while maintaining satisfactory in-district accuracy. |
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent work shows that deep learning models are sensitive to low-level correlations between simple features and specific output labels, leading to over-fitting and lack of generalization. |
| Approach: | They propose to eliminate single-word correlations altogether to mitigate this problem . they highlight several alternatives to dataset balancing to enhance contexts . |
| Outcome: | The proposed approach to balancing datasets is insufficient, the authors argue . they suggest enhancing datasets with richer contexts and abstaining from interaction . |
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2020.tacl-1)
Copied to clipboard
| 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. |
Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective (2025.naacl-long)
Copied to clipboard
| Challenge: | In text classification tasks, models often rely on spurious correlations for predictions, incorrectly associating irrelevant features with the target labels. |
| Approach: | They propose a Causally Calibrated Robust Classifier which integrates a causal feature selection method based on counterfactual reasoning and an unbiased inverse propensity weighting (IPW) loss function. |
| Outcome: | The proposed method achieves state-of-the-art performance among methods without group labels and can compete with the models that utilize group labels. |