Ecologically Valid Explanations for Label Variation in NLI (2023)

Abstract: Human label variation, or annotation disagreement, exists in many natural language processing (NLP) tasks, including natural language inference (NLI). To gain direct evidence of how NLI label variation arises, we build LiveNLI, an English dataset of 1,415 ecologically valid explanations (annotators explain the NLI labels they chose) for 122 MNLI items (at least 10 explanations per item). The LiveNLI explanations confirm that people can systematically vary on their interpretation and highlight within-label variation: annotators sometimes choose the same label for different reasons. This suggests that explanations are crucial for navigating label interpretations in general. We few-shot prompt large language models to generate explanations but the results are inconsistent: they sometimes produces valid and informative explanations, but it also generates implausible ones that do not support the label, highlighting directions for improvement.

## introduction practices for operationalizing annotations in nlp datasets have largely assumed one single label per item. however, human label variation @xcite has been found in a wide range of nlp tasks @xcite @xcite @xcite . @xcite , i.a., argued that variation that is systematic in annotations should be considered signal, not noise. specifically, the nli task -identifying whether a hypothesis is true (entailment), false (contradiction), or neither (neutral) given a premise -has embraced label variation and set out to predict it @xcite . however, it remains an open question where label variation in nli stems from. * now at google. we introduce the livenli foot_0 dataset, containing 122 re-annotated mnli @xcite items, each with at least 10 highlights and free-text explanations for the labels chosen by the annotators, totaling 1,415 explanations. the dataset is relatively small because we intended it to be suitable for few-shot evaluation, following recent trends in @xcite . the contribution of livenli's explanations is their ecological validity: annotators provide both the label and the explanations, in contrast to many explanation datasets (e.g., e-snli @xcite ) in which explanations were collected for a given "ground truth" label. livenli hence provides direct evidence for how label variation among annotators arises. in addition, livenli highlights another kind of variation, within-label variation: in some cases, humans converge to the same label for different reasons. finally, we evaluate whether gpt-3 (brown et al., 2020) can generate explanations for label variation. we found that its explanations can be fluent and informative but also implausible or not supporting the label. ## data collection we re-annotated 122 items from the mnli dev set where each item originally had 5 annotations: procedure annotators read the premise (context) and hypothesis (statement), and were asked whether the statement is most likely to be true/false/either true or false (corresponding to entailment/contradiction/neutral, respectively). multiple labels were allowed. they were then asked to write a free-text explanation for all the label(s) they chose. inspired by @xcite ; @xcite ; tan (2022), we asked annotators to give explanations that provide new information and refer to specific parts of the sentences, and to avoid simply repeating the sentences. annotators were also asked to highlight words from the premise/hypothesis most relevant for their explanations. 48 native speakers of english were recruited from surge ai (surgehq.ai, details in a1). ## analysis of livenli label descriptive statistics we normalize each multilabel response into a distribution, then average the individual distributions to obtain the item's label distribution. figure 1 , where each set of stacked bars gives the distributions for one item, shows that label variation is widespread, even on the 12 items that received full agreement in mnli (right panel). the annotators provided legitimate explanations for the labels that differ from the original one (see (1) in a1), suggesting that the variation is genuine and not noise. this reiterates baan et al. (2022)'s point that considering one distribution to govern all judgments from the population is a simplification, and distributions can shift with annotator pools. annotators' reasons for label variation jiang and de marneffe (2022) analyzed the reasons for nli label variation from a linguists' perspective and built a taxonomy of linguistic phenomena that can lead to different interpretations. for instance, in ex.1 ( for each pair of premise/hypothesis, one author assigned it one or more taxonomy categories exhibited in its explanations ( furthermore, the explanations uncover more ways in which label variation can occur. in particular, we found that label variation often stems from annotators judging the truth of different atissue content, which answers different questions under discussion (quds) @xcite . for example, in ex.1, responses 1.1 and (the first half of) 1.2 take the main point of the hypothesis to be stephanopoulos having a very large pundit following, but have different judgments on whether this main point is true. on the other hand, 1.3 and 1.4 focus on the reason for which pundits follow stephanopoulos, and agree with 1.1 that he has a large following. we thus added a "qud" category to the taxonomy. 2 the qud category occurred in 28 items (out of 122) and is the third most frequently used category, thus an important aspect of language understanding. incorporating qud into nlp tasks and modeling @xcite is an interesting direction. within-label variation our analysis reveals that there is also within-label variation: annotators may vary in their text understanding when giving the same label. there are 16 instances of within-label variation: annotators chose the same labels but provided very different explanations. 3 as mentioned earlier, 1.2 and 1.3 both chose neutral but assumed different quds (1.2 also encompasses 1.3). as another example, in ex.2, the explanations indicate that the annotators differ in whether to take the premise and hypothesis to be referring to the same entity/event. response 2.1 assumes that the premise and hypothesis refer to the same season: since the premise talks about winter and not summer, the hypothesis i'm so sick of summer is false. 2.2, on the other hand, does not assume that the premise and the hypothesis refer to the same season, and infers through probabilistic enrichment that the speaker likes summer, making the hypothesis false. thus both with and without the coreference assumption, annotators label the item as contradiction. within-label variation shows up in ∼13% of livenli, and highlights the diversity of human explanations. this suggests that model generated explanations should be similarly diverse to imitate human decision-making. ## predict and explain label variation models should capture inherent label variation to achieve robust understanding. explanations are essential for navigating the uncertainty rising from such variation. here we use livenli to few-shot prompt lms to jointly predict and explain label variation. we used text-davinci-003, a variant of gpt-3 (brown et al., 2020) with few-shot prompting (example in we use predict-then-explain (p-e) and explainthen-predict (e-p), a.k.a. chain-of-thought prompting @xcite , which has been shown to improve performance of reasoning tasks like nli @xcite . (p)redict-only prompts the model without explanations for the same items, giving a baseline measuring the effect of explanations. train/test items since jiang and de marneffe (2022) found that models are sensitive to label imbalance, we used a label-balanced set of 11 training items from livenli, with one item per singlelabel and two items per multiple-label. the test set includes the rest of livenli, augmented with chaosnli for infrequent labels, totaling 259 items. ## conclusion and discussion we built livenli providing ecologically valid explanations for nli items with label variation. we showed that livenli's explanations help us understand where label variation stems from, and identify question under discussion as an additional source of label variation. we further emphasize a deeper level of variation: within-label variation. our work illustrates the utility of explanations and provides future avenues for improving model capability of leveraging and generating explanations.