V ari E rr NLI : Separating Annotation Error from Human Label Variation (2024)

Abstract: Human label variation arises when annotators assign different labels to the same item for valid reasons, while annotation errors occur when labels are assigned for invalid reasons. These two issues are prevalent in NLP benchmarks, yet existing research has studied them in isolation. To the best of our knowledge, there exists no prior work that focuses on teasing apart error from signal, especially in cases where signal is beyond black-and-white.To fill this gap, we introduce a systematic methodology and a new dataset, VariErr (variation versus error), focusing on the NLI task in English. We propose a 2-round annotation procedure with annotators explaining each label and subsequently judging the validity of label-explanation pairs.VariErr contains 7,732 validity judgments on 1,933 explanations for 500 re-annotated MNLI items. We assess the effectiveness of various automatic error detection (AED) methods and GPTs in uncovering errors versus human label variation. We find that state-of-the-art AED methods significantly underperform GPTs and humans. While GPT-4 is the best system, it still falls short of human performance. Our methodology is applicable beyond NLI, offering fertile ground for future research on error versus plausible variation, which in turn can yield better and more trustworthy NLP systems.

## introduction labeled data plays a crucial role in modern machine learning (ml) (e.g., @xcite . data quality impacts ml performance and, in turn, user trust. it is therefore of vital importance to aim at high-quality consistently-labeled benchmark data (e.g., @xcite . however, recent research revealed a notable presence of annotation errors in widely-used nlp benchmarks @xcite . similar observations were made recently in computer vi- * equal contribution. contradiction entailment neutral nli item labels explanations tommy is not hurt but rather bad strong emotion a picture hit tommy's head with force ambiguous if tommy hurt himself or another guy annotator a annotator b premise: as he stepped across the threshold, tommy brought the picture down with terrific force on his head. hypothesis: tommy hurt his head bringing the picture down. sion (cv) @xcite @xcite . at the same time, there is increasing evidence that for many items in many tasks, more than a single label is valid. for some items, systematic variation exists for valid reasons, such as plausible disagreement or multiple interpretations. in other words, the world is not just black and white. human label variation (hlv, as termed by plank 2022) has been shown on a wide range of nlp tasks @xcite @xcite , including in natural language inference (nli; @xcite . nli involves determining whether a hypothesis is true (entailment), false (contradiction), or neither (neutral), assuming the truth of a given premise; see figure 1 for an example with plausible labels. although high-quality, consistently labeled data may initially appear to conflict with the goal of accommodating hlv, it is important to emphasize that we do not perceive these as contradictory goals. while hlv exists, so do errors. we assert that annotators are inevitably prone to make errors, such as misunderstanding instructions or accidentally selecting a wrong label. optimizing data quality is essential through providing clear instructions and effective training, and identifying annotation errors yields better datasets @xcite . however, still little is known about what constitutes an error versus plausible variation. we lack both a theory and operationalizable procedures to tease apart error from plausible hlv consistently and soundly. some datasets with errors (and their corrections) exist, and there has been work on automatic error detection (aed). however, both have their limitations ( §2). a crucial gap remains: a lack of examination in real-world scenarios where the signal is nuanced, not merely black-and-white. to address this gap, this paper contributes: (i) varierr, a novel multi-annotator english nli dataset with both plausible variation and detected errors. to the best of our knowledge, no such dataset exists yet. (ii) a new methodology to detect errors: we collect multiple annotations, where each label comes with an ecologically valid explanation inspired by @xcite , and propose to pair them with validity judgments to identify errors. (iii) finally, we benchmark existing aed methods and gpts in a challenging setup, where the task is to tease apart error from plausible human label variation. our findings indicate that existing aed methods underperform humans and gpts substantially on our self-validated varierr nli dataset. we release our data and code to facilitate uptake. 1 ## related work labeled data is the fuel of machine learning, as it drives both learning and evaluation. following a data-centric view, we focus on improving data quality over data quantity @xcite @xcite . we aim to bring together work on data quality from two ends: annotation error vs. human label variation. annotation errors and aed several recent work found errors in widely used benchmarks, such as conll 2003 for named entity recognition @xcite @xcite , tacred for relation extraction @xcite , wsj for syntax @xcite , and ima-genet for object classification @xcite @xcite . flaggers and scorers for aed have been proposed @xcite . flaggers detect errors by providing a hard decision on whether an instance is erroneous. scorers, on the other hand, assign a score to each instance reflecting the likelihood of being an error, and the top-n scoring instances are then corrected. here, we focus on scoring methods to rank instances. most of the aed work mentioned has limitations as they either rely on posthoc mining of errors (and might therefore miss out on errors) in semi-automatic ways (e.g., @xcite , or they inject synthetic noise which has been shown to result in datasets where errors are easy to spot @xcite . instead of using synthetic noise, we focus on realistic setups and re-annotate data in ecologically valid ways. ## varierr: annotation procedure to tease apart human label variation from error, we create varierr (variation versus error), a nli dataset with two rounds of annotations by four annotators: foot_0 round 1 for nli labels and explanations ( §3.1) and round 2 for validity judgments ( §3.2). ## automatic error detection (aed) on varierr we now describe our experiments to detect annotation errors using varierr automatically. we evaluate the capabilities of aed methods, llms, and human heuristics (all henceforth scorers) in capturing annotation errors. ## conclusion errors exist in datasets, but so does plausible human label variation. this paper defines a general procedure to separate the two by leveraging ecologically valid explanations (where annotators provide their reasons for a label) and pairing these with annotators' validations (to allow corrections). we provide a new varierr dataset for the task of nli re-annotated from scratch. our empirical investigation on varierr for nli finds that traditional annotation error detection methods fare poorly and underperform humans and llms. while this paper only applies our 2-round annotation procedure, varierr, to nli data, our methodology is general, and we hope it inspires uptake. future work includes adapting these approaches to other nlp tasks, probing differences between self-and peer-judgments, mapping such strategies to (large) language models, and linking varierr to experiments with llms' explainability, self-correction, or multi-agent systems.