Detecting Label Errors by Using Pre-Trained Language Models (2022)

Abstract: We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task loss significantly outperforms more complex error-detection mechanisms proposed in previous work. To this end, we contribute a novel method for introducing realistic, human-originated label noise into existing crowdsourced datasets such as SNLI and TweetNLP. We show that this noise has similar properties to real, hand-verified label errors, and is harder to detect than existing synthetic noise, creating challenges for model robustness.We argue that human-originated noise is a better standard for evaluation than synthetic noise. Finally, we use crowdsourced verification to evaluate the detection of real errors on IMDB, Amazon Reviews, and Recon, and confirm that pre-trained models perform at a 9–36% higher absolute Area Under the Precision-Recall Curve than existing models.

## introduction improving model performance in the presence of label errors comprises an area of active research @xcite . however, existing methods focus on label errors in training data. although seldom acknowledged, evaluation label errors are at least as pernicious as training label errors: pervasive errors in commonly used nlp benchmarks have been found to destabilize model performance @xcite . such findings suggest that improving training methods does not preclude the need for improving the underlying data. we propose a simple method for using large, pre-trained language models * equal contribution. (llms) to directly identify label errors for the purposes of correcting or removing them. the majority of work in identifying label errors, and in data-centric artificial intelligence (dcai) more broadly, focuses on image and healthcare data @xcite . however, the success of the foundation model (fm) paradigm in applying pre-trained language models to a variety of nlp tasks @xcite suggests that fms may be a powerful tool for detecting and correcting label errors in language datasets. pre-training has been shown to imbue models with properties such as resistance to label errors, class imbalance @xcite , out-of-distribution detection @xcite , and confidence calibration (desai and durrett, 2020), while conferring ro- ## related work learning with noisy labels (lnl) focuses on the model-training stage. noise-robust approaches examine model enhancements such as the design of loss functions @xcite @xcite , regularization @xcite , reweighting @xcite , hard negative mining and contrastive learning @xcite . noise-cleansing approaches aim to segregate clean data from noisy data in training, e.g. bagging and boosting @xcite , k-nearest neighbors @xcite , outlier detection @xcite , bootstrapping @xcite , and neural networks supervised directly on detecting an error, when such data exist @xcite . lnl methods have in most cases been evaluated using artificially-generated label noise. a typical evaluation of an lnl method uses a standard benchmark dataset, and programmatically corrupts training labels via one of three main noising schemes (frenay and verleysen, 2014; algan and ulusoy, 2020). uni-form noise is most commonly used but unrealistic; deep neural networks have been found to perform well even when noised labels outnumber original labels at a ratio of 100 to 1 @xcite . class-dependent noise randomly permutes labels based on a confusion matrix. however, research on annotator disagreement suggests that label errors tend to result from feature-based, not class-based ambiguity @xcite . training models to generate realistic feature-based or instance-dependent noise has recently emerged as an area of active research @xcite @xcite . however, algan and ulusoy (2020) report that feature-dependent noise may bias benchmark performance toward similar models to the ones used to generate this noise. the noising schemes above each fail in some way to simulate organic, naturally occurring label errors, which are estimated to occur in common benchmarks at 1-5% of labels @xcite @xcite or even as much as 20% @xcite . for organic errors, cl @xcite predicts errors in imdb, amazon reviews, and other datasets by estimating a joint distribution between noisy and uncorrupted labels; @xcite pioneers using bert for error detection on conll-2003 via a classifier trained over a frozen bert embeddings layer. ## generating realistic label noise to better evaluate label noise detection performance, we prepare a set of benchmark datasets populated with controllable, highly realistic, human-originated label noise. sources of human error. we observe that datasets often undergo multiple annotation passes: crowdsourced labels typically aggregate several annotators' inputs @xcite , and subsets of data may receive more extensive validation @xcite , gold labels by trained experts @xcite , or correction passes @xcite . we hypothesize that differences between such annotations may be usefully repurposed as a source of realistic, human-originated label noise, as disagreements between annotators is known to reflect systematic ambiguity and human error @xcite , and differs from the type of noise studied using existing synthetic methods. we construct three noising methods which may be applied in many of the above scenarios. for any dataset which includes two levels of label quality, the dissenting label method replaces final labels with disagreeing labels at random, simulating imperfect quality control. datasets which provide individual annotator identifiers may apply the dissenting worker approach: select one annotator at random, apply all of their labels which disagree with final labels, and repeat until reaching the target noise rate. this simulates gaps in annotator training, which introduce systematic idiosyncrasies. finally the crowd majority method applies to any dataset in which individual annotations can be aggregated to produce a label other than the final label: the former label simulates challenging, systematic errors in the latter. noising and robustness. we assess the effect of these noising methods using tweetnlp @xcite , a corpus of 26,435 tokens from 1,827 american english tweets collected from twitter used to train part-of-speech (pos) tagging. tweetnlp includes gold labels annotated by 17 experts, but later received a separate crowdsourced assessment, aggregated by majority vote @xcite . we noise tweetnlp to eight levels from 0-20% separately for each method, fine-tune deberta-v3-base @xcite on each noising, and evaluate models on both noisy and clean test sets. results from noisy test sets represent model performance as measurable in 0 2 4 6 8 losses of noised test items 0.0 0.2 0.4 0.6 density uniform noise class-based noise dissenting label dissenting worker crowd majority clean data points practice; real datasets contend with noise in evaluation data. clean test set results represent true model performance. fig. 3 reports the results of this evaluation. for uniform and class-dependent noise, true performance remains high even for high noise levels (per @xcite . but crucially, this robustness does not extend to humanoriginated noise: human label errors are correlated to input text, and so contain systematic erroneous features, which models may learn in training. on more challenging noising methods, although measured performance appears to increase, true performance actually linearly decreases with noise. fig. 4 explores this further via the distributions of model losses for each noising method: loss induced by human-originated noise overlaps significantly with clean items, whereas loss from uniform and class-based noising is distinctively higher. ## validation on real label errors in addition to human-originated noise datasets, we evaluate error detection performance on organic errors in two benchmark datasets, following northcutt et al. (2021a)'s protocol. ## experiments label noise realism. section 4 defined the human-originated noising protocol used to generate tweetnlp-5, tweetnlp-m, and snli-5. section 5 specified a protocol for identifying organic label errors present in imdb and amazon. we assess the realism of synthetic noise methods by comparing loss distributions against models trained with organic noise (for real label errors, we refer to items verified as correctable via mturk). we quantify the degree to which noising induces erroneous learning by measuring the wasserstein distances between noisy and clean loss distributions. overall llm performance. we assess broad error detection capabilities by evaluating 13 commonly-used llms on tweetnlp-5. we measure performance against loss, model size, and glue score (a proxy for general model capability; @xcite . appendix d provides implementation details. this experiment's results inform model selection: we use deberta-v3-base for all further experiments. foot_1 main experiment. using our realistic noising benchmarks, and the mturk baselines and verification protocol, we can now assess the performance of each label error detection method. we evaluate foundation model loss (fml) and foundation model ensembling (fme). as a baseline, we evaluate confident learning (cl; @xcite . cl is not a standalone method; it augments existing models. given an underlying model's predicted scores for each class and the true proportion of each class, cl forms a reweighting matrix, called the confident joint. to form a label error prediction score, cl reweights the model's scores by the confident joint. cl hypothesizes items in order of this resulting score. for each dataset, we run 25 hyperparameter sweeps which each fine-tune a model for the given task (e.g., pos tagging) using noisy data, and select the model with the best validation set task performance. we report label error detection performance (not task performance). area under the precsion-recall curve (aupr) provides an overall performance score @xcite . we also report metrics representing performance on competing data cleaning priorities: efficiency requires high precision on a small number of items, whereas coverage requires high recall on a larger number of items. appendix e.1 describes the truncated aupr used for imdb and amazon, which are too costly to fully crowd verify. end-to-end noising. we finally isolate the effects of noise and label error correction for validation and test splits. for each dataset, we prepare three versions of the validation and test splits, respectively: a clean version assumed to contain zero errors, foot_3 a noisy version, with label noise deliberately introduced, and a corrected version generated from noisy splits using our main error detection method (ranking errors with fme and correcting the top err% data points). we train 40 hyperparameter sweeps, with performance cross-evaluated on all prepared data splits. we report three different metrics. we report each model's accuracy on the clean test split as the true accuracy. following the norms of fig. 3 , we report the measurable accuracy as the accuracy of the model selected using performance on the noisy or corrected validation split on the corresponding test split. finally, we report the rank of the model as the rank of the model's performance on clean test data. the best performing model among all sweeps has rank 1, and the worst has rank 40. this metric emphasizes that different validation sets select different models. we perform this exercise using imdb and amazon noised to 5% (i-5, a-5), and tweetnlp-5 and tweetnlp-m. ## discussion rapid data "health check". sorting evaluation data by each item's loss is an easy way to quickly highlight label errors. using this simple technique with a foundation model appears to generally identify over half of all label errors through human re-evaluation of a single-digit percentage of all data ( eval. test perf. i-5 a-5 t-5 t-m noisy measurable 90.1 88.3 89.3 89.3 true 94.2 91.0 92.8 82.0 rank 10 1 3 10 corr. measurable 95.1 90.7 92.9 88.5 true 95.1 90.8 93.0 82.0 rank 4 5 2 8 clean true 95.8 91.0 93.8 82.1 pre-training and robustness. we demonstrate that despite established findings on artificial noising @xcite , pretraining confers limited robustness to realistic human noise. the majority of label errors are systematic in nature @xcite @xcite , and crowdsourced labels form, to an extent, a different distribution from reality, as approximated by expert labels @xcite . when trained on crowdsourced or other data containing systematic errors, fms quickly drift towards this incorrect distribution. applying ai to data-centric ai. datacentric ai aims to improve ai through labeling, curating, and augmenting the underlying data. we find that ai itself can be applied towards improving data quality, as part of a human-inthe-loop (hitl) iteration, which contributes an additional positive feedback loop between data quality and ai performance. new challenges in lnl. standard noising methods are unrealistic and no longer challenging for state-of-the-art language models (algan and ulusoy, 2020); recent lnl analyses study conditions where up to 80% of labels are noised @xcite . our findings reinforce the need to reassess lnl methods in the context of more realistic noise @xcite . our human-originated noising method produces realistic label errors, and can be applied to any crowdsourced dataset which includes raw annotation data. as such datasets emerge across deep learning domains @xcite , we hope this method may inspire challenging and realistic new lnl performance benchmarks. our method also enables detailed exploration of the properties of human noise, which may support work on open lnl problems such as improving feature-based noising techniques, and estimating dataset noise @xcite . ## conclusions and future work pre-trained models effectively identify label errors on real nlp datasets, definitively outperforming existing methods on the same benchmarks by an absolute 9-36% in aupr. human-originated noising techniques may present a solution to the clear limitations of current lnl noising schemes: they are highly realistic and yet controllable for experimental purposes. we invite further exploration of this family of label noising techniques. we believe human-originated noising enables future advancements across multiple areas of lnl, supporting new tasks and metrics in areas such as the cost of human reannotation, estimation of dataset error, and mitigation of bias. finally, we advocate for lnl to move towards an end-to-end approach of evaluating with label noise, which takes into account noise within validation and test splits, and more accurately models the conditions of data in practice.