ALVIN : Active Learning Via IN terpolation (2024)

Abstract: Active Learning aims to minimize annotation effort by selecting the most useful instances from a pool of unlabeled data. However, typical active learning methods overlook the presence of distinct example groups within a class, whose prevalence may vary, e.g., in occupation classification datasets certain demographics are disproportionately represented in specific classes. This oversight causes models to rely on shortcuts for predictions, i.e., spurious correlations between input attributes and labels occurring in well-represented groups. To address this issue, we propose Active Learning Via INterpolation (ALVIN), which conducts intra-class interpolations between examples from under-represented and well-represented groups to create anchors, i.e., artificial points situated between the example groups in the representation space. By selecting instances close to the anchors for annotation, ALVIN identifies informative examples exposing the model to regions of the representation space that counteract the influence of shortcuts. Crucially, since the model considers these examples to be of high certainty, they are likely to be ignored by typical active learning methods. Experimental results on six datasets encompassing sentiment analysis, natural language inference, and paraphrase detection demonstrate that ALVIN outperforms state-of-the-art active learning methods in both in-distribution and out-of-distribution generalization.

## introduction despite the remarkable zero-shot and few-shot learning capabilities of large language models (llms) @xcite @xcite , supervised fine-tuning remains a critical component of model development @xcite @xcite . collecting high-quality labeled data is, nonetheless, time-consuming and labor-intensive @xcite . to address this annotation bottleneck, active learning (al) seeks to select the most useful instances from a pool of unlabeled data, thereby maximizing model performance subject to an annotation budget @xcite . however, datasets commonly used for model fine-tuning often contain shortcuts @xcite @xcite , i.e., spurious correlations between input attributes and labels present in a large number of examples @xcite . for example, in occupation classification datasets, many examples exhibit patterns that incorrectly associate certain demographics, such as race and gender, with specific occupations @xcite . consequently, models exploiting shortcuts achieve high performance on well-represented example groups, but fail on under-represented groups where shortcuts do not apply @xcite . this issue is particularly prominent in out-of-distribution settings, where under-represented groups can become more prevalent due to distribution shifts @xcite . by neglecting the presence of these distinct example groups in the training data, al methods amplify the prevalence of well-represented groups, thereby exacerbating shortcut learning @xcite . motivated by these shortcomings, we introduce active learning via interpolation (alvin). the key idea behind alvin is to leverage interpolations between example groups to explore the representation space. specifically, we identify unlabeled instances for annotation by assessing their proximity to anchors, i.e., artificial points in the representation space created through intra-class interpolations between under-represented and wellrepresented examples. intuitively, alvin selects informative instances with features distinct from those prevalent in well-represented groups, helping the model avoid reliance on shortcuts. importantly, because these instances are deemed high certainty by the model, they are often overlooked by typical al methods. we conduct experiments on six datasets spanning sentiment analysis, natural language inference, and paraphrase detection. our results demonstrate that alvin consistently improves out-of-distribution generalization compared to several state-of-the-art al methods, across different dataset acquisition sizes, while also maintaining high in-distribution performance. we analyze alvin to gain deeper insights into its performance improvements. first, we examine the unlabeled examples identified by alvin, showcasing its ability to select diverse, high-certainty instances while avoiding outliers that could negatively impact performance. next, through several ablation studies, we demonstrate the advantages of our interpolation strategy compared to other interpolation-based al methods. finally, we explore the impact of hyper-parameters on performance and assess the computational runtime required to select instances for annotation. ## experimental setup datasets we conduct experiments on six datasets across sentiment analysis, natural language inference, and paraphrase detection. in line with previous works in al @xcite @xcite , we use sa @xcite , nli @xcite , anli @xcite , sst-2 @xcite , imdb @xcite , and qqp @xcite . to assess out-of-distribution (ood) generalization we use semeval-2017 task 4 @xcite for sa, anli for nli, and nli for anli, imdb for sst-2, sst-2 for imdb, and twitterp-pdb @xcite • uncertainty @xcite acquires annotations for unlabeled instances with the highest predictive entropy according to the model. ## related work active learning al methods can be categorized into three groups, informativeness-based, representativeness-based, and hybrid al approaches @xcite . informativenessbased al approaches typically measure the usefulness of unlabeled instances via uncertainty sampling @xcite , expected gradient length @xcite , and bayesian methods @xcite . recent al works examine informativeness from the perspective of contrastive examples @xcite , model training dynamics @xcite , and adversarial perturbations @xcite . representativeness-based al approaches like core-sets @xcite , discriminative active learning @xcite , and clustering-based methods @xcite aim to select diverse instances such that the underlying task is better specified by the labeled set. finally, hybrid al approaches combine these two paradigms either by switching between informativeness and representantivess @xcite , or by first creating informativeness-based representations of the unlabeled instances and then clustering them @xcite . compared to prior work using interpolations for al @xcite , alvin differs in two key ways: (1) we opt for interpolations between specific labeled instance pairs, rather than randomly interpolating labeled and unlabeled instances, and (2) we sample λ from a beta distribution beta(α, α) instead of optimizing it for each pair individually. this approach grants us greater control over the placement of the anchors in the representation space, ensuring they are positioned nearer to either under-represented or well-represented example groups as required. mixup alvin is inspired by mixup @xcite , a popular data augmentation method originally explored in the field of computer vision. mixup generates synthetic examples by interpolating random pairs of training examples and their labels. recent mixup variants conduct interpolations using model representations @xcite , dynamically compute the interpolation ratio @xcite , explore different interpolation strategies @xcite , and combine mixup with regularization techniques @xcite . in the context of nlp, @xcite apply mixup on word and sentence embeddings using convolutional and recurrent neural networks. conversely, @xcite propose a mixup variant that conducts interpolations on the input text. park and caragea (2022) apply mixup to calibrate bert and roberta models, while @xcite propose tmix, a mixup-inspired semi-supervised objective for text classification. ## conclusion in this work, we propose alvin, an active learning method that uses intra-class interpolations between under-represented and well-represented examples to select instances for annotation. by doing so, alvin identifies informative unlabeled examples that expose the model to regions in the representation space which mitigate the effects of shortcut learning. our experiments across six datasets, encompassing a broad range of nlp tasks, demonstrate that alvin consistently improves both indistribution and out-of-distribution accuracy, outperforming other state-of-the-art active learning methods.