REV : Information-Theoretic Evaluation of Free-Text Rationales (2023)

Abstract: Generating free-text rationales is a promising step towards explainable NLP, yet evaluating such rationales remains a challenge. Existing metrics have mostly focused on measuring the association between the rationale and a given label. We argue that an ideal metric should focus on the new information uniquely provided in the rationale that is otherwise not provided in the input or the label. We investigate this research problem from an information-theoretic perspective using conditional V-information (Hewitt et al., 2021). More concretely, we propose a metric called REV (Rationale Evaluation with conditional V-information), to quantify the amount of new, label-relevant information in a rationale beyond the information already available in the input or the label. Experiments across four benchmarks with reasoning tasks, including chain-of-thought, demonstrate the effectiveness of REV in evaluating rationale-label pairs, compared to existing metrics. We further demonstrate REV is consistent with human judgments on rationale evaluations and provides more sensitive measurements of new information in free-text rationales. When used alongside traditional performance metrics, REV provides deeper insights into models’ reasoning and prediction processes.

## introduction model explanations have been indispensable for trust and interpretability in natural language processing (nlp) @xcite @xcite @xcite . free-text rationales, which explain a model prediction in natural language, have been especially appealing due to their flexibility in eliciting the reasoning process behind the model's decision making @xcite @xcite @xcite , making them closer to human explanations. however, existing metrics for free-text rationale evaluation remain narrowly focused on the extent to which a rationale can help a (proxy) model predict the label it explains (i.e., accuracy based) @xcite . these metrics offer little understanding of the new information contained in the rationale, as added to the original input, that could explain why the label is selectedthe very purpose a rationale is designed to serve. for instance, the two rationales r * 1 and r1,a in fig. 1 would be considered equally valuable under existing metrics, even though they supply different amount of novel and relevant information. in this paper, we overcome this shortcoming by introducing an automatic evaluation for free-text rationales along two dimensions: (1) whether the rationale supports (i.e., is predictive of) the intended label, and (2) how much new information does it provide to justify the label, beyond what is contained in the input. for example, rationale r1,b in fig. 1 violates (1) because it is not predictive of the label, "enjoy nature". rationale r1,a does support the label but contains no new information that justifies it, beyond what is stated in the input x; thus, it violates (2). rationale r * 1 is satisfied along both dimensions: it supports the label and does so by providing new and relevant information, beyond what is in the input. our proposed evaluation is designed to penalize both r1,a and r1,b , while rewarding rationales like r * 1 . we introduce rev 2 , which adapts an figure 1: our evaluation framework for different free-text rationales (r). r * 1 is a human-written rationale, r1,a and r1,b are two generated rationales for the true label y 1 . our metric, rev, based on cvi @xcite is able to distinguish all three rationales by measuring how much new and label-relevant information each adds over a vacuous rationale, b; performance-based evaluations can only distinguish between r1,a and r1,b . for an (arguably) incorrect label, y 2 , rev still gives a positive score highlighting that r2 is able to provide new information for why it supports y 2 . prediction accuracy can be augmented with rev to provide a fuller interpretability of model decisions. @xcite , which quantifies the degree of information contained in a representation beyond another (baseline) representation, accessible to a model family v. as our baseline representation, we consider any vacuous rationale which simply (and declaratively) combines an input with a given label, without providing any new information relevant to answering why the label was chosen. rev adapts conditional v-information to evaluate rationales, where we compare two representations-one from an evaluation model trained to produce the label given the input and the rationale, and the other from another evaluation model for the same task but considering only the input (disguised as a vacuous rationale). other metrics do not take into consideration vacuous rationales, and are hence unable to measure new and label-relevant information in rationales. in our experiments, we present evaluations with rev for rationales under two reasoning tasks, commonsense question-answering (cqa; @xcite and natural language inference (nli; @xcite , across four benchmarks. several quantitative evaluations demonstrate the capabilities of rev in providing evaluations along new dimensions for free-text rationales, while also being more consistent with human judgements compared to existing metrics. we also provide comparisons to demonstrate the sensitivity of rev to various degrees of input perturbations. additionally, evaluation with rev offers insights into why rationales obtained through chain-of-thought prompting @xcite do not necessarily improve prediction performance. ## rev: information-theoretic evaluation of rationales we introduce a new metric, rev, rationale evaluation with conditional v-information, for evaluation of free-text rationales on the proposed dimensions ( §2.2), based on the framework of conditional v-information ( §2.1). we consider the setting where we have input @xmath0, label @xmath1, and free-text rationale @xmath2. a common strategy to evaluate rationale r is through an evaluator function f ∶ @xmath3, which maps a variable z to a label distribution. here, z can be defined based on the evaluation framework; e.g., z can be a concatenation of x and r, or contains only x. these metrics evaluate the utility of r based on how much r helps f predict y . the evaluator f is typically trained on a set of input, label and rationale triples d @xmath4, y j , r j )}, and applied to d @xmath5, y i , r i )} for evaluation. the utility of r is formulated as the difference between the performance of the evaluator on predicting y with r, and without it, i.e. figure 1 illustrates this issue via an example. here, accuracy-based evaluation can distinguish between r1,a and r1,b since r1,a supports y 1 and r1,b does not. however, it is unable to distinguish between r * 1 and r1,a (since both are predictive of y 1 ), despite the fact that r1,a does not provide any unique and relevant information to answer why the label should be y 1 . in practice, vacuous rationales such as r1,a are commonly seen in model generations @xcite . this calls for an evaluation metric which is able to identify and penalize such vacuous rationales. ## experimental setup we outline our experimental setup by describing the reasoning tasks and datasets ( §3.1), followed by the task and evaluation models ( §3.2), and the baseline metrics for comparison ( §3.3). additional details on the setup are provided in appendix b. ## evaluating rev we first compare rev with existing metrics ( §4.1) and human judgments ( §4.2) on the ecqa dataset, as well as show rev on other cqa and nli benchmarks. we then test the sensitivity of different metrics to input perturbations ( §4.3). next, we apply rev to generations via few-shot prompting (4.4). additional experiments are listed in appendix c. vs. accuracy); hence, our focus remains on the ranking over different sources of rationale-label pairs. results are shown in figure 2 (left panel). all three metrics rank the crowdsourced rationales (y * ;r * ) in ecqa the highest. while by definition, rev for vacuous rationales (y * ;b) is low, both las and rq scores for these rationales are quite high, showing that these metrics are incapable of measuring the amount of additional information in rationales. intuitively, we expect weaker rationalelabel consistency in @xmath6, as the labels are forcefully selected among the candidates as opposed to being freely generated by the task model ( §3.2). while rev is able to capture this intuition and ranks @xmath7, las and rq have a different ranking. qualitative results comparing all three metrics are provided in we additionally analyze rev for "inputirrelevant rationales": sentences extracted from a knowledge base that contain the ground-truth labels but do not necessarily explain the labels w.r. ## related work model rationales broadly fall into two categories: extractive rationales and free-text rationales. extractive rationales contain some important features extracted from input texts that make models produce final predictions @xcite @xcite . free-text rationales are produced by generative models in the form of natural language. compared to extractive rationales, free-text rationales explain model predictions in a more human-like way and fill the gap in explaining reasoning tasks @xcite @xcite @xcite . evaluations on extractive rationales have been well studied, generally from two perspectivesfaithfulness and plausibility @xcite @xcite . faithfulness measures to which extent rationales reflect the true reasoning process of models, while plausibility evaluates how convincing rationales are to humans @xcite . other perspectives include the ability of rationales in helping a student model simulate a teacher model @xcite or bridging the communication between a classifier and a layperson @xcite . existing automatic metrics for free-text rationales focus on rationale-label association, and measure the utility of a rationale based on how much it helps a model proxy predict the given label (inspired by human simulatability @xcite ) @xcite or the gold label @xcite given the input. @xcite further propose a framework to evaluate the automatic metrics. however, none of them consider measuring the amount of additional new information in free-text rationales. @xcite conduct a human study on the additional knowledge provided by free-text rationales. this work is the first that proposes an automatic metric to quantify the new information in free-text rationales. ## conclusion we introduce rev, an information-theoretic measure to evaluate the amount of new, label-relevant information in free-text rationales, beyond the information contained in the input. we empirically demonstrate the advantage of rev compared to existing metrics focusing simply on label-rationale association, and show that rev is more consistent with human judgments. rev also offers insights into evaluating rationales generated via few-shot prompting. while we recommend the usage of rev alongside traditional performance metrics, future work might explore a combined metric to measure the correctness of a prediction as well as the informativeness of the rationale towards this prediction. ultimately, free-text rationales are for the benefit of human users and there exist multiple criteria for human utility of rationales @xcite , beyond label relevance and informativeness.