RORA : Robust Free-Text Rationale Evaluation (2024)

Abstract: Free-text rationales play a pivotal role in explainable NLP, bridging the knowledge and reasoning gaps behind a model’s decision-making. However, due to the diversity of potential reasoning paths and a corresponding lack of definitive ground truth, their evaluation remains a challenge. Existing metrics rely on the degree to which a rationale supports a target label, but we find these fall short in evaluating rationales that inadvertently leak the label . To address this problem, we propose RORA, a RObust free-text RAtionale evaluation against label leakage. RORA quantifies the new information supplied by a rationale to justify the label. This is achieved by assessing the conditional \mathcal{V} -information (Hewitt et al., 2021) with a predictive family robust against leaky features that can be exploited by a small model. RORA consistently outperforms existing approaches in evaluating human-written, synthetic, or model-generated rationales, particularly demonstrating robustness against label leakage. We also show that RORA aligns well with human judgment, providing a more reliable and accurate measurement across diverse free-text rationales.

## introduction the ability of large language models (llms) to generate free-text rationales that elaborate on their decision-making processes holds promise for explainable nlp, either in the form of a reasoning chain @xcite or posthoc explanations @xcite . previous works have also collected humanwritten rationales to enhance model reasoning and the generation of free-text rationales @xcite @xcite . however, evaluating these rationales remains an open problem because of the diversity of reasoning ♡ equal contribution 1 https://github.com/zipjiang/rora paths and the lack of definitive ground truth @xcite . as a result, existing metrics rely on measuring how much the rationale supports a given label. this is usually achieved by comparing predictions of models trained with and without rationales. for example, leakage-adjusted simulatability (las) @xcite and rationale quality (rq) @xcite ) measure rationale quality through the difference in accuracy. alternatively, rationale evaluation with conditional-vinformation (rev) @xcite evaluates the reduction in model predictive uncertainty upon conditioning on the rationale. yet all these methods are vulnerable to label leakage @xcite @xcite : the rationale inadvertently paraphrasing or restating labels, creating a spurious shortcut @xcite for the evaluation model to infer the label. the critical issue stems from the mismatch in objectives: existing methods evaluate how easy it is to utilize information in the rationale, but rationales are explanations, whose quality does not always come with simplicity. the best explanation has to support the answer through some sense of mechanisms, such as methodically considering a set of axioms and running through a deductive chain @xcite @xcite , without which they are mere "effects" @xcite . figure 1 shows an example where existing evaluation methods are highly sensitive to label leakages in paraphrased or restated rationales, while in fact, these label leakages merely increase the predictive probability without providing any meaningful explanations. 2 with this objective in mind, we introduce rora, a novel approach to evaluate rationales robust to label leakage. rora's construction consists of existing baselines are highly sensitive to rationales that simply restate the label or paraphrase the given question and label, leading to inflated scores compared to the human-annotated rationale. in contrast, rora provides an informativeness score that better characterizes rationale quality. it is achieved by 1 detecting potential leakage tokens in the rationale ( §3.1) and 2 generate additional training data with counterfactual editing for data augmentation ( §3.2), followed by 3 training an evaluation model invariant to label leakage ( §3.3). we compare rora with baseline metrics @xcite @xcite in evaluating various synthetic and humanannotated rationales, with or without label leakage, on three qa datasets. rora consistently outperforms baseline metrics by providing robust evaluations against label leakages. we also compare rora against model-generated rationale evaluation and demonstrate its better agreement with human evaluation. ## motivation and backgrounds following previous work by @xcite , rora adopts the framework of conditional v-information @xcite . in this section, we outline the general idea of this framework and point out how the objective of rora is critically different from previous attempts. ## rora for rationale evaluation we describe our framework (figure 1 ) to estimate equation 5 as a measurement of the informativeness of rationales. ## experiments and results to demonstrate the effectiveness of rora, we evaluate human-annotated, synthetic, and modelgenerated rationales on two distinct qa tasks. for synthetic data, we (1) write a sentence that verbatim leaks the label (leaky); (2) perturb the humanannotated rationales (gold) to include the leaky rationale (gold leaky ); (3) create declarative rewrites (vacuous) of the question-answer pairs using a question rewriter @xcite . 7 without further specification, we use the gradient attribution @xcite from fast-text model @xcite to identify labelleaking tokens to mask. we use t5-base @xcite to generate counterfactual data. finally, two additional t5-base models are trained under equation 7 and equation 8 as evaluation models to compute the rora score using equation 9 . 9 we compare rora with three strong baselines: las @xcite , rq @xcite , and rev @xcite . all baselines use t5-base as the evaluation model. note that the boundaries of these metrics differ depending on the choice of the h v function in equation 1. specifically, las and rq, which utilize accuracy, lie within the [-1, 1] interval. in contrast, rora and rev, employing the conditional v-entropy (equation 2), have a theoretical boundary of @xcite . because of different scales and evaluation criteria, our analysis mainly focuses on the ranking over different rationale types, as well as the relative difference within each metric. we also do an ablation study with (rora ablation ) with all the attributions, counterfactual generation and invariant learning removed. this is equivalent to the formulation in equation 3 in which the evaluator φ is directly trained on the given rationales and questions to maximize the likelihood of labels. ## conclusions we introduce rora, an innovative approach designed to enhance the accuracy and robustness of rationale quality evaluation amidst the challenges posed by label leakage. our method stands out for its adaptability across diverse model architectures and hyperparameter configurations, delivering consistent rationale quality evaluations that align more closely with human evaluation.