REV: Information-Theoretic Evaluation of Free-Text Rationales

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 2007–2030 July 9-14, 2023 ©2023 Association for Computational Linguistics REV: Information-Theoretic Evaluation of Free-Text Rationales Hanjie Chen♡∗ Faeze Brahman♠♢ Xiang Ren♠♣ Yangfeng Ji♡ Yejin Choi♠♢ Swabha Swayamdipta♣ ♡Department of Computer Science, University of Virginia ♠Allen Institute for AI ♣University of Southern California ♢ Paul G. Allen School of Computer Science & Engineering, University of Washington {hc9mx,yangfeng}@virginia.edu {faezeb,xiangr,yejinc}@allenai.org swabhas@usc.edu 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 fo- cus on the new information uniquely provided in the rationale that is otherwise not provided in the input or the label. We investigate this re- search problem from an information-theoretic perspective using conditional V-information (Hewitt et al., 2021). More concretely, we pro- pose 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 ef- fectiveness of REV in evaluating rationale-label pairs, compared to existing metrics. We fur- ther demonstrate REV is consistent with hu- man 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.1 1 Introduction Model explanations have been indispensable for trust and interpretability in natural language pro- cessing (NLP) (Ribeiro et al., 2016, 2020; Lipton, 2018; Chen et al., 2020, 2021a). Free-text ratio- nales, which explain a model prediction in natural language, have been especially appealing due to their flexibility in eliciting the reasoning process be- hind the model’s decision making (Camburu et al., ∗Work done during an internship at AI2. 1Our code is publicly available at https://github.com/ HanjieChen/REV 2018; Narang et al., 2020; Rajani et al., 2019; Ku- mar and Talukdar, 2020; Brahman et al., 2021), making them closer to human explanations. How- ever, existing metrics for free-text rationale eval- uation remain narrowly focused on the extent to which a rationale can help a (proxy) model predict the label it explains (i.e., accuracy based) (Hase et al., 2020; Wiegreffe et al., 2021). 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 selected— the very purpose a rationale is designed to serve. For instance, the two rationales r∗ 1 and ˆr1,a in Fig. 1 would be considered equally valuable under ex- isting 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 ra- tionales along two dimensions: (1) whether the ra- tionale supports (i.e., is predictive of) the intended label, and (2) how much …

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