Fighting Bias with Bias: Promoting Model Robustness by Amplifying
Dataset Biases
- URL: http://arxiv.org/abs/2305.18917v1
- Date: Tue, 30 May 2023 10:10:42 GMT
- Title: Fighting Bias with Bias: Promoting Model Robustness by Amplifying
Dataset Biases
- Authors: Yuval Reif, Roy Schwartz
- Abstract summary: Recent work sought to develop robust, unbiased models by filtering biased examples from training sets.
We argue that such filtering can obscure the true capabilities of models to overcome biases.
We introduce an evaluation framework defined by a bias-amplified training set and an anti-biased test set.
- Score: 5.997909991352044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: NLP models often rely on superficial cues known as dataset biases to achieve
impressive performance, and can fail on examples where these biases do not
hold. Recent work sought to develop robust, unbiased models by filtering biased
examples from training sets. In this work, we argue that such filtering can
obscure the true capabilities of models to overcome biases, which might never
be removed in full from the dataset. We suggest that in order to drive the
development of models robust to subtle biases, dataset biases should be
amplified in the training set. We introduce an evaluation framework defined by
a bias-amplified training set and an anti-biased test set, both automatically
extracted from existing datasets. Experiments across three notions of bias,
four datasets and two models show that our framework is substantially more
challenging for models than the original data splits, and even more challenging
than hand-crafted challenge sets. Our evaluation framework can use any existing
dataset, even those considered obsolete, to test model robustness. We hope our
work will guide the development of robust models that do not rely on
superficial biases and correlations. To this end, we publicly release our code
and data.
Related papers
- Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning [0.2812395851874055]
This paper proposes a comprehensive approach using multiple methods to remove bias in AI models.
We train multiple models with the counter-bias of the pre-trained model through data splitting, local training, and regularized fine-tuning.
We conclude our solution with knowledge distillation that results in a single unbiased neural network.
arXiv Detail & Related papers (2024-02-01T09:24:36Z) - Improving Bias Mitigation through Bias Experts in Natural Language
Understanding [10.363406065066538]
We propose a new debiasing framework that introduces binary classifiers between the auxiliary model and the main model.
Our proposed strategy improves the bias identification ability of the auxiliary model.
arXiv Detail & Related papers (2023-12-06T16:15:00Z) - Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo
Chamber [17.034228910493056]
This paper presents experimental analyses revealing that the existing biased models overfit to bias-conflicting samples in the training data.
We propose a straightforward and effective method called Echoes, which trains a biased model and a target model with a different strategy.
Our approach achieves superior debiasing results compared to the existing baselines on both synthetic and real-world datasets.
arXiv Detail & Related papers (2023-05-06T13:13:18Z) - Debiasing Vision-Language Models via Biased Prompts [79.04467131711775]
We propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding.
We show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models.
arXiv Detail & Related papers (2023-01-31T20:09:33Z) - Feature-Level Debiased Natural Language Understanding [86.8751772146264]
Existing natural language understanding (NLU) models often rely on dataset biases to achieve high performance on specific datasets.
We propose debiasing contrastive learning (DCT) to mitigate biased latent features and neglect the dynamic nature of bias.
DCT outperforms state-of-the-art baselines on out-of-distribution datasets while maintaining in-distribution performance.
arXiv Detail & Related papers (2022-12-11T06:16:14Z) - General Greedy De-bias Learning [163.65789778416172]
We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space.
GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
arXiv Detail & Related papers (2021-12-20T14:47:32Z) - Learning from others' mistakes: Avoiding dataset biases without modeling
them [111.17078939377313]
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended task.
Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available.
We show a method for training models that learn to ignore these problematic correlations.
arXiv Detail & Related papers (2020-12-02T16:10:54Z) - Improving Robustness by Augmenting Training Sentences with
Predicate-Argument Structures [62.562760228942054]
Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective.
We propose to augment the input sentences in the training data with their corresponding predicate-argument structures.
We show that without targeting a specific bias, our sentence augmentation improves the robustness of transformer models against multiple biases.
arXiv Detail & Related papers (2020-10-23T16:22:05Z) - Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
This paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases.
First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method.
The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features.
arXiv Detail & Related papers (2020-05-10T17:56:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.