PECO : Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers (2023)

Abstract: Building natural language inference (NLI) benchmarks that are both challenging for modern techniques, and free from shortcut biases is difficult. Chief among these biases is “single sentence label leakage,” where annotator-introduced spurious correlations yield datasets where the logical relation between (premise, hypothesis) pairs can be accurately predicted from only a single sentence, something that should in principle be impossible. We demonstrate that despite efforts to reduce this leakage, it persists in modern datasets that have been introduced since its 2018 discovery. To enable future amelioration efforts, introduce a novel model-driven technique, the progressive evaluation of cluster outliers (PECO) which enables both the objective measurement of leakage, and the automated detection of subpopulations in the data which maximally exhibit it.

## introduction natural language inference (nli) is a fundamentally pairwise task, wherein a logical relation between two statements is predicted. progress on nli benchmarks is an important proxy for advancements in natural language reasoning by machines. models are trained to process the two statements simultaneously, in a paired sentence condition (psc). unfortunately many modern nli datasets exhibit single sentence label leakage. when this leakage is present, models are able to accurately predict the pairwise relation encoded by the labels in a single sentence condition (ssc)-where the model is only shown one of the statements @xcite . this is a serious problem, rendering nli datasets' capture of reasoning questionable, and limiting the robustness of models trained on them. nli is formalized as predicting a relation @xmath0, entail, contradict} from a pair of sentences (premise s 1 and hypothesis s 2 ). an ideal nli benchmark without single sentence label leakage will have distribution of r that is conditionally ## analyzing nli dataset bias in this section we introduce quantification techniques for more accurately characterizing the extent of these bias problems in the aforementioned nli datasets, analyze how they interact with the observable bias itself, and develop tools for producing future nli benchmarks that more closely resemble the ideal benchmark. ## results as discussed above, ideal nli benchmarks are neither saturated nor biased. unfortunately, as when the psc model and ssc models "reason" more similarly for a dataset, the relation leakage bias exhibited in that dataset tends to be higher. ## discussion some examples are only correctly classified in the single-sentence condition. a common assumption to discussions of cheating features in machine learning is that they play a role in inflated classification accuracy when present. however, anli r3 provides an interesting counterexample. for this dataset, the hypothesis-only model achieves a ssc accuracy of 48.1%, and the psc model achieves 49.8% (a %r r of 90%), and sota achieves 53%. despite this score similarity, the samples which the two conditions are able to actually classify correctly vary surprisingly. with an nbr of 58.5%, only ≈ 27% of test samples are correctly classified by both the single and two sentence condition models. this means that around 21% of samples in a3 test are only correctly classified by the single-sentence model. perhaps unsurprisingly, anli exhibits the lowest tra out of all datasets tested, indicating that it is somewhat of an outlier in having the ssc and psc condition models reason differently on it. ## conclusion in the half decade since @xcite single sentence relation leakage bias has proven to remain a difficult issue. efforts to debias nli have led to datasets that merely exhibit different kinds of bias than those shown before, or less saturated benchmarks that continue to exhibit cheating features. future work must prioritize reducing observable bias directly using a model-driven approach.