No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference (2023)

Abstract: Natural Language Inference (NLI) has been a cornerstone task in evaluating language models’ inferential reasoning capabilities. However, the standard three-way classification scheme used in NLI has well-known shortcomings in evaluating models’ ability to capture the nuances of natural human reasoning. In this paper, we argue that the operationalization of the neutral label in current NLI datasets has low validity, is interpreted inconsistently, and that at least one important sense of neutrality is often ignored. We uncover the detrimental impact of these shortcomings, which in some cases leads to annotation datasets that actually decrease performance on downstream tasks. We compare approaches of handling annotator disagreement and identify flaws in a recent NLI dataset that designs an annotator study based on a problematic operationalization. Our findings highlight the need for a more refined evaluation framework for NLI, and we hope to spark further discussion and action in the NLP community.

## introduction with the rise of large language models like gpt-3 @xcite , palm @xcite , and gpt-4, foot_0 it has become increasingly necessary to evaluate their language understanding and reasoning abilities. one influential task in this regard is natural language inference (nli) @xcite , which is used to examine the inferential and commonsense reasoning skills of language models @xcite . nli requires a model to determine the relationship between a statement, known as the premise p , and another statement, called the hypothesis h, by classifying it as entailment (h must be true given p), contradiction (h must be false given p), or neutral (h can or cannot be figure 1 : selected nli items from snli with annotations (shown by colors). the diamonds on the right show the gold label for these items in snli; note item 4 is marked '-' and is not assigned a gold label (hence it is ignored). we argue that items with all four annotation distributions should be considered neutral, but that there should be at least two sub-types of neutral. solving nli requires the ability to perform textual inference between any two sentences (and in some cases, between any two arbitrarily long texts), making it a versatile framework for developing and evaluating reasoning benchmarks. many nlp tasks, like question answering @xcite , dialog systems @xcite , machine translation @xcite , identifying biased or misleading statements @xcite , fake news detection @xcite , paraphrase detection @xcite , and fact verification @xcite , require understanding and reasoning about the meaning of text and can be re-framed as nli problems. nli provides a broad framework for studying and alleviating logical inconsistencies in a language model's reasoning @xcite including explanation-based maieutic prompting @xcite , that uses nli to evaluate individual links in a reasoning chain. most nli datasets @xcite @xcite utilize crowdsourcing to either generate nli items or gather labels for pre-existing items. while this approach has advanced research on textual entailment, we believe that current nli datasets, both established and recent, have overlooked important issues in their annotation design that hinder their validity as measures of textual entailment. although the effects of different crowdsourcing schemes for nli dataset development has been studied @xcite , we focus on a specific issue: the operationalization of neutral. neutral items usually have the lowest levels of annotator agreement @xcite , and we contend that this disagreement has been handled improperly in previous work, contributing to the ongoing debate about how to handle disagreement in nli @xcite @xcite . instructions provided to annotators for labeling items as neutral are often ambiguous and inconsistent between datasets, with phrases like "neither" @xcite or "might be correct" @xcite . we believe these problems can be addressed by reconsidering the prevailing operationalization of neutral and replacing it with one which embraces disagreement. although we are not the first to argue for the importance of properly incorporating disagreement @xcite @xcite @xcite , we identify specific problems introduced by ignoring disagreement (for example, by dropping examples with low agreement entirely), and offer new evidence supporting its adoption grounded in the psychometric concept of construct validity. consider the items shown in figure 1 , sourced from the snli dataset @xcite . a general consensus on the gold label is reached by the annotators in the first three items, but the fourth item exhibits a high degree of disagreement. while the first three items are labeled neutral in snli and used to train models, the fourth is labeled with a special '-' class, indicating an irresolvable level of disagreement, and hence it is removed from training data @xcite . this practice (also used by @xcite effectively treats disagreement as an undesirable product of nli data collection-a linguistic annotation artifact to be considered as noise rather than signal. but what is the source of this disagreement? should item 4 in figure 1 be ignored, or is it simply a different form of neutrality? we argue that item 4 should be considered a different sense of neutral than the one represented by item 1, because two interpretations are possible: (1) the individuals in the embrace may be facing in opposite directions, resembling a conventional embrace, and (2) one individual may be embracing the other from behind, thereby causing them to face the same direction. this ambiguity in how to interpret such items leads to two irreconcilable types of neutrals; items can be either true neutrals (item 1 in figure 1 ), or they can be neutral as a result of conflicting interpretations (item 4). ## operationalizing neutral in nli, the neutral label is used for situations where the relationship between the premise and hypothesis is ambiguous or there is insufficient information to determine the relationship. neutral is often considered a catch-all for relationships that do not fall under entailment or contradiction. the definition of neutral is typically provided to crowd-source workers as "neither" @xcite or "might be correct" @xcite . but is a classification of neutral simply a default assumption that always means neither entailment nor contradiction can be definitively determined, or can it be a positive claim that a different type of relationship holds between the sentences? a closer look at the data obtained from nli datasets suggests that neutrality is more complex than it may initially seem. according to @xcite , neutral items in many nli datasets exhibit the lowest agreement levels. the most frequent label below an agreement level of @xmath0.8 for both the snli and multinli subsets is neutral, while it is the least frequent label at a perfect agreement level. this lack of agreement motivates our focus on neutral particularly, as it is consistently the most problematic label to annotate. the empirical study in section 3 also shows that a neutral-based classification scheme has a better separability than a disagreement-based classification scheme. there are at least two senses in which the relationship between two sentences can be said to be neutral, which become clear if we imagine two possible justifications that an individual nli annotator may provide for why they selected the label neutral: (1) true neutral: the annotator cannot find any sufficiently strong reasons (using whichever standard of strength they determine appropriate) to satisfy either entailment or contradiction; or (2) conflicting neutral: the annotator finds strong reasons to support both entailment and contradiction. it is a central position of this paper that these two interpretations of the neutral label are irreconcilable and should not be confused with each other. attempting to conflate the two, e.g. by assuming that neutrality is simply the mid-point on a continuous scale between the two extremes of entailment and contradiction, will and has led to significant reductions in quality of data collections and their resulting benchmark datasets (see §5). no existing nli dataset, to our knowledge, asks or encourages annotators to explain whether their reasons for selecting neutral are in line with true or conflicting neutral as we have defined them above. for the present work, then, we present evidence for the discriminant validity of true and conflicting neutral (i.e., that they refer to two distinct constructs that can and should be measured separately @xcite ) by assuming that they will be approximately reflected in the distribution of individual annotations on a single nli item-in other words, conflicting neutral items will tend to have annotation distributions resembling item 4 in figure 1 , whereas true neutrals will tend to match item 1. results in section 3 show that indeed such a classification scheme does a much better job of separating the four classes for models than a scheme that conflates all three labels. ## an analysis of unli we have argued that a carefully grounded operationalization of the neutral label is crucial for ensuring the reliability (performance should be free from random error) and validity of nli. to demonstrate the issues that can arise if this caution is not taken, we next analyze a recent nli dataset -uncertain nli (unli) @xcite . the unli dataset, when used for fine-tuning, appears to actually harm downstream performance @xcite @xcite . unli attempts to enhance nli by converting the categorical labels for some snli items to a continuous scale. participants were instructed to rate the likelihood of a given hypothesis being entailed by a given premise using an ungraduated slider, ranging from 0 (labeled as "impossible") to 1 (labeled as "very likely") and were shown the probability they were assigning to the premise-hypothesis pair in real time. according to @xcite , the probabilistic nature of nli @xcite suggests that not all contradictions or entailments are equally strong. 8 thus, unli was developed with the intention of capturing subtler distinctions in entailment strength using a continuous scale. this dataset has over 60k items from snli, annotated by humans. for each premise-hypothesis pair, two annotations were collected, and in cases where the first two annotators differed by 20% or more, a third annotator was consulted. however, the dataset only reports the averaged scores, which makes it impossible to assess the degree of agreement or correlation between the two annotators or even identify examples where a third annotator was needed. thus, reported values near 0.5 (which we might take to be the equivalent of neutral items) fundamentally conflate items where both annotators chose the midpoint on the slider with items where each annotator chose one of the extremities. the assumption that one continuous scale can capture even the three categories in standard nli (entailment, contradiction, and neutral) is a strong one (already shown to be problematic in @xcite ), which is typically glossed over by presuming that entailment lies at the higher end of the spectrum, contradiction at the other end, and neutral somewhere in the middle. but no such instruction to interpret the scale this way was provided to annotators. indeed, as we will show, annotators appeared to be confused as to whether an absence of entailment meant that the slider should be at the '0' position, or in the middle. in their attempt to obtain subjective probabilities for premise-hypothesis pairs, the authors used a scale with 10k steps with a scaled logistic transformation (f (x) = σ(β(x -5000))) to convert the values on the scale into probabilities between 0 and 1. they do not report the chosen value of β and do not specify whether the scores were averaged before or after applying the function, which is crucial information as both would yield different results. because raw values of x are not provided, and we do not know whether scaling is performed before or after averaging, we are unable to recover the chosen values of β. the scale chen et al. (2020) used was based on easl @xcite , an approach developed to collect scalar ratings in nlp tasks. 9 they then modified the easl scale by utilizing the aforementioned logistic transformation, which they argued would allow for more nuanced values near both extremes. notably, the source of the anchor points used on the scale (i.e., "impossible" and "very likely") is not explicitly stated by @xcite , although it is possible they were obtained from joci @xcite , a dataset created for studying ordinal commonsense reasoning that uses the same anchor points for opposite ends of the scale. 10 in effect, their logistic transformation compresses the extreme ends of the scale, so that the graphic they display (figure 1 in chen et al. ( 2020 )), at first glance, appears as if the nli items labeled as contradiction, neutral, and entailment occupy roughly equal space across the continuum of values. figure 3 instead depicts the distribution of averaged human responses collected by @xcite on a linear scale. 11 it is clear to observe in figure 3 that while entailment and contradiction annotations are distinctly separated and skewed heavily towards the extreme opposite ends of the scale, annotations for neutral span the entire range from 0 to 1. the origin of this discrepancy is unclear, but based on the instructions given to them, it may be that annotators were unsure where to place neutral on the scale. supporting this hypothesis is the bulge near 0 on the violin plot for neutral in figure 3 , which suggests that annotators chose 0 for both neutral and contradiction items. this information is obscured by the logistically transformed graph displayed by @xcite . measurement issues are not uncommon in other fields that routinely run human studies, including psychological and educational mesurement. development of annotation schemes in these fields often involves careful consideration of the item premise hypothesis snli annotations unli score a woman with a blue jacket around her waist is sitting on the ledge of some stone ruins resting. a man sits on a ledge. 4c -0n -1e 0.88 a lady is standing up holding a lamp that is turned on. she is lighting a dark room. 2c -2n -1e 0.78 a singer wearing a leather jacket performs on stage with dramatic lighting behind him. a singer is on american idol. 1c -4n -0e 0.01 a small boy wearing a blue shirt plays in the kiddie pool. boy cooling off during the summer. 1c -4n -0e 0.89 ## conclusion in this paper, we examined the operationalization of neutral in nli datasets. our analysis revealed that previous attempts to handle ambiguity in nli based on neutrality have significant issues with their validity as annotation strategies for nli. we proposed a new operationalization of neutral into true neutral and conflicting neutral. although instances of these forms of neutral are present in most popular nli datasets, they have been conflated into one neutral label, limiting our ability to measure ambiguity in nli effectively. we showed that this approach of casting nli to a 4-way classification task is better than the disagreement-based classification scheme used in previous work. we used unli as a case study to highlight measurement and annotation issues that should be avoided in the future. of the many factors that make science successful, two of the most important are the ability to make carefully designed measurements, and replicability. the first of these cannot be met when measurements of constructs are made in ways that significantly compromise their validity and reliability. and replicability is made impossible when papers are published in reputable venues reporting unclear collection details, having important parameter choices omitted, and with datasets reporting summary statistics in place of crucially important data. a significant roadblock of the work we reported in this paper was the lack of availability of individual annotations in widely-adopted nli benchmarks, even when there seems to be no public benefit in leaving out such information. it is our hope that the present work will encourage our fellow ai researchers to more highly value such considerations.