F air F low: Mitigating Dataset Biases through Undecided Learning for Natural Language Understanding (2024)

Abstract: Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing framework called FairFlow that mitigates dataset biases by learning to be undecided in its predictions for data samples or representations associated with known or unknown biases. The framework introduces two key components: a suite of data and model perturbation operations that generate different biased views of input samples, and a contrastive objective that learns debiased and robust representations from the resulting biased views of samples. Experiments show that FairFlow outperforms existing debiasing methods, particularly against out-of-domain and hard test samples without compromising the in-domain performance.

## introduction existing computational models developed for natural language processing (nlp) tasks are vulnerable to dataset biases and spurious correlations in data, often referred to as "shortcuts." these shortcuts enable models to achieve high performance on nlp datasets by exploiting surface-level correlations between features and labels. however, they also result in a significant performance drop on hard or slightly modified test data @xcite . for example, in the area of natural language inference (nli), models like bert @xcite tend to misclassify premise-hypothesis pairs that contain "negation" words in their hypotheses as "contradiction," which happen to be predictive features associated with the contradiction label in certain nli datasets @xcite @xcite . existing debiasing approaches can detect known (clark et al., 2019; sanh et al., 2021; 1 our code is available at https://github.com/ clu-uml/fairflow . @xmath0. @xmath1. intact input + target model (p, h) @xmath2, h) zero out 90% content figure 1 : an example highlighting the concept of "undecided learning" using two types of data perturbation techniques. given a premise-hypothesis pair in nli, the model is expected to correctly classify their entailment relationship. however, given only the hypothesis, a robust model should be undecided, i.e., refrain from making a definite judgment about the relationship between an unknown premise and the given hypothesis. similarly, given a severely corrupted representation, a robust model should be undecided about the relation between a corrupted premise and hypothesis pair. models that retain confidence in assigning labels to such inputs are likely to rely on shortcuts. fairflow takes an undecided stance against such inputs. karimi @xcite and previously unidentified or unknown @xcite biases in training data. they mitigate dataset biases by re-weighting examples @xcite , learning robust representations @xcite , learning robust feature interaction patterns @xcite , or reducing the effect of biased model components @xcite . despite the significant progress made in addressing dataset biases, existing models have certain limitations: (a): they often adopt a single view to dataset biases and primarily focus on specific types of biases @xcite . however, rich sources and diverse types of dataset biases can be present in the data. we tackle the above challenges by developing fairflow-a multiview contrastive learning framework that mitigates dataset biases by being undecided in its prediction for biased views of data samples (see figure 1 ). specifically, the proposed method employs several data perturbation operators to generate biased views of intact data samples and integrate them into the training data and learning process. when presented with biased inputs, the model is trained to be undecided about the possible labels by making a uniform prediction across the label set. at the same time, the model is encouraged to be confident about intact inputs, which often serve as a reference for unbiased samples. therefore, the approach encourages learning representations that are more attentive to the true signal of the underlying tasks rather than relying on shortcuts that are specific to certain datasets. in addition, the inherent randomness of the implicit perturbations in fairflow ( §2.4.1) exposes the model to a diverse range of perturbations and prevents it from overfitting to specific types of biases present in the data. the contributions of this paper are: the experimental results show that fairflow obtains substantial improvement over competing models. specifically, it achieves an average performance gain of 10.2 points on stress test datasets across several nlu tasks while maintaining performance on the original test sets. in addition, models trained using our framework show strong transferability, resulting in an average gain of 3.7 points in transfer testing experiments across different datasets and domains. furthermore, we show that existing methods can be further improved by incorporating the proposed perturbation operators within their original objectives, resulting in a substantial average improvement of 5.8 points on stress test sets across datasets. ## results and discussions robust debiasing model the main results in in addition to better debiasing performance, our approach shows stronger transferability compared to baselines. specifically, fairflow outperforms debiasmask, kernelwhitening, and e2e-poe on transfer test set by 2.7, 2.1 and 2.1, respectively. in addition, fairflow-poe and fair-flow-focal retain strong transfer performance as well, indicating that our framework does not hurt models' transferability. comparing different fusion techniques in the last three rows in more bias branches, less biased model unlike existing approaches that have a single view to dataset biases, our model employs multiple views, allowing it to effectively capture and mitigate various types of biases present in the data. specifically, compared to e2e-poe which only captures one sub-input bias, fairflow-poe achieves on average 1.8, 9.5 and 3.8 absolute points improvement on id, stress and ood test set across three different datasets. both methods employ poe as the fusion technique. compared to debiasmask @xcite which only captures bias though a weak model, fairflow-poe achieves 1.5, 12.3 and 11.0 points improvement on id, stress and ood test sets, respectively. branches contribute differently to examine the contribution of each perturbation branch, we conduct ablation studies on mnli. specifically, we add one branch at a time to the vanilla model or remove one branch at a time from the full model, see in addition, we investigate the effect of different combinations of perturbations. specifically, we train our model with one explicit perturbation and one implicit perturbation at a time. figure 3 illustrates the relative increase of performance to standard fine-tuning across id, stress and ood test sets. two combinations yields better results on the ood test set. the first combines droppremise or drophypothesis with droplayer, while the second combines perturbation of all inputs (e.g. shuffle) and purturbrep. the improved results likely stem from the complementary strengths of these diverse perturbation techniques, which can create a more robust debiasing model. debiased models are still biased our results in fairflow maintains generalization across biases bias in existing methods may be because of their tendency to over-specialize in specific types of biases. efficiency we evaluate the efficiency of different debiasing methods in terms of number of trainable parameters and training time. as ## conclusion we investigate bias mitigation in nlu datasets by formulating the debiasing problem within a contrastive learning framework, incorporating explicit and implicit perturbation techniques and introducing undecided learning. through extensive experiments across a range of nlu tasks, we demonstrate the effectiveness of our method in achieving improved debiasing performance, while maintaining performance on in-domain test sets. we find that existing methods (including ours) are still sensitive to dataset biases, and our experiments show the limitations of these approaches in fully addressing dataset biases. these results necessitate investigating a more systematic evaluation benchmark for debiasing. our approach can potentially be improved by investigating more complex biases @xcite , exploring alternative training paradigms such as curriculum learning @xcite , and evaluating robustness to unseen biases @xcite . beyond nlu, our work can potentially be applied to a broader range of applications @xcite .