Debias NLU Datasets via Training-free Perturbations (2023)

Abstract: Several recent studies have shown that advanced models for natural language understanding (NLU) are prone to capture biased features that are independent of the task but spuriously correlated to labels. Such models often perform well on in-distribution (ID) datasets but fail to generalize to out-of-distribution (OOD) datasets. Existing solutions can be separated into two orthogonal approaches: model-centric methods and data-centric methods. Model-centric methods improve OOD performance at the expense of ID performance. Data-centric strategies usually boost both of them via data-level manipulations such as generative data augmentation. However, the high cost of fine-tuning a generator to produce valid samples limits the potential of such approaches. To address this issue, we propose PDD, a framework that conducts training-free Perturbations on samples containing biased features to Debias NLU Datasets. PDD works by iteratively conducting perturbations via pre-trained mask language models (MLM). PDD exhibits the advantage of low cost by adopting a training-free perturbation strategy and further improves the label consistency by utilizing label information during perturbations. Extensive experiments demonstrate that PDD shows competitive performance with previous state-of-the-art debiasing strategies. When combined with the model-centric debiasing methods, PDD establishes a new state-of-the-art.

## introduction although recent language models have demonstrated impressive performance on many natural language understanding (nlu) benchmarks @xcite , several studies show that models tend to leverage dataset biases for inference @xcite . such biases † corresponding author. previous solutions to resolve the issue can be roughly separated into two types: model-centric methods and data-centric methods @xcite . model-centric methods boost the robustness of models by designing new training objectives or model architectures to force the models into paying less attention to the biases during training, at the expense of id performance @xcite . data-centric methods conduct data-level manipulations to debias the original datasets @xcite @xcite . recently, data-centric debiasing methods, especially those through generative augmentations, are gaining increasing attention due to their ability to enhance both id and ood performance simultaneously. however, existing data-centric methods based on generative augmentations are still faced with the limitation of high cost @xcite . these methods usually take effort to finetune a large task-specific generator, such as a gpt-2 @xcite , on large-scale nlu datasets to generate new samples. additionally, they inevitably require costly retraining once the dataset varies, further increasing the computational costs. to address the limitation of existing data-centric debiasing methods, we propose pdd (perturbing samples containing biased features to debias nlu datasets), a novel and cost-effective debiasing framework for nlu datasets based on a trainingfree perturbation strategy. to reduce expenses, we adopt a pre-trained mask language model (mlm) to perturb samples rather than finetuning a generator to generate new samples. specifically, we perturb the samples containing biased features to mitigate the strong statistical correlations between such features and the labels, which is measured by z-statistics @xcite . moreover, we design label-specific prompts to incorporate label information into perturbations and further adopt a confidence filter step to improve label consistency. to verify the effectiveness of pdd, we carry out experiments on three nlu tasks: natural language inference @xcite , fact verification @xcite , and paraphrase identification. experimental results demonstrate that pdd shows comparable ood performance to previous state-of-the-art debiasing methods. since pdd is data-centric, we enhance it with model-centric methods and observe further improvements in performance, exceeding any previous state-of-the-art to the best of our knowledge. our code and data are available at: https://github.com/guoqi2000/debias_pdd . in summary, our contributions include: biases in datasets: biases in nlu benchmarks are idiosyncratic artifacts introduced in the annotation processes and are often modeled as spurious correlations between simple features and labels. several works trying to provide a theoretical framework to measure such spurious correlations. gururangan et al. (2018) involves pointwise mutual information (pmi) to quantify the spurious correlations in datasets. veitch et al. (2021) formalize spurious correlations in a causal framework and consider the model's prediction should be invariant to perturbations of the spurious feature. gardner et al. (2021) assume a uniform distribution obeyed by the prediction conditioned on any single feature and figure out spurious features with hypothesis testing. ## experimental setup to validate the effectiveness of pdd, we conduct experiments on three nlu tasks: natural language inference, fact verification, and paraphrase identification. we compare pdd with existing state-ofthe-art debiasing strategies on several ood challenging datasets @xcite @xcite @xcite . ## results experimental results on mnli-hard, hans, fever and qqp are shown in ## conclusions we propose pdd, a novel and cost-effective debiasing framework for nlu datasets adopting a training-free perturbation strategy to mitigate the statistical correlations between biased features and labels. in contrast with existing data-centric strategies, pdd shows applicability to various biases in different datasets at a low cost. extensive experiments demonstrate its effectiveness over existing baselines. when combined with model-centric methods, pdd further improves the ood performance, achieving a new state-of-the-art. future work may concern extending pdd with automatic identifications of biased features.