ZARA : Improving Few-Shot Self-Rationalization for Small Language Models (2023)

Abstract: Language models (LMs) that jointly generate end-task answers as well as free-text rationales are known as self-rationalization models. Recent works demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. However, the ability to benefit from explanations only emerges with large-scale LMs, which have poor accessibility. In this work, we explore the less-studied setting of leveraging explanations for small LMs to improve few-shot self-rationalization. We first revisit the relationship between rationales and answers. Inspired by the implicit mental process of how human beings assess explanations, we present a novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to automatically construct pseudo-parallel data for self-training by reducing the problem of plausibility judgement to natural language inference. Experimental results show ZARA achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric. In addition, we conduct human and quantitative evaluation validating ZARA’s ability to automatically identify plausible and accurate rationale-answer pairs.

## introduction driven by the concerns of whether the decisions made by the artificial intelligence models are trustworthy, providing free-text, natural language explanations (nles) has drawn substantial attention in the research community @xcite @xcite @xcite . comparing with popular explanation techniques within the input scope, e.g., attributing feature importance scores to tokens @xcite or extracting fragments of text highlights @xcite ; jain et al., 2020), free-text explanation foot_0 is more expressive, inherently apt for human comprehension and brings richer information in addition to input context @xcite . yet, the construction of nle datasets is expensive and challenging due to quality control issues such as inconsistency and under-specification (wiegreffe and @xcite . the development of interpretable nlp systems which can provide nles in few-shot is necessitated. recent works @xcite @xcite achieve few-shot self-rationalization, i.e., jointly generating freetext explanations and end-task labels, by extending the usage of nles to compose chain-of-thought (cot) input-rationale-output demonstrations for prompt-based learning. comparing with standard prompting (i.e., without rationales), prompting with rationale-augmented exemplars triggers lm's complex reasoning ability, significantly boosting the end-task performance. however, the main drawback is that only excessively large lms (generally 100b-plus) demonstrate this ability to leverage explanations, which sharply emerges when scaling model size sufficiently @xcite . in this work, we explore the less-studied setting of improving few-shot self-rationalization only relying on affordable, small lms (200m∼2.7b). we adopt self-training @xcite , a simple yet effective methodology that is not practical for large lms in most real-world scenarios. we first investigate the relationship between the generated explanations and end-task predictions, and find plausible explanations are usually paired with correct label predictions. namely, plausibility is a strong indicator for answer correctness. motivated by this finding, we propose zero-shot augmentation of rational-answer pairs (zara) for self-training. specifically, we reduce the problem of assessing rationale plausibility to the task of natural language inference (nli), and propose a zero-shot plausibility approximator towards automatic assessment of the generated rationales, without requiring any ground-truth labels or golden explanations. the approximator can be viewed as an agent for plausibility judgement. as illustrated in figure 1 , to determine the plausibility of the rationale, humans implicitly ask themselves whether they can draw conclusions to the predicted answer by understanding the task, the input question, and the supported rationale with their logic and reasoning. to approximate such process explicitly, the approximator leverages the ability of textual entailment to yield a probability score indicating the explanation plausibility. connecting to the self-training paradigm, we first train a self-rationalization model by fewshot prompt-based learning with natural language prompts, and leverage the approximator to collect pseudo-parallel data, i.e, unlabeled inputs paired with high-confident rationale-answer pairs, for creating an augmented training set which is then used to learn an improved self-rationalization model. with various small-size lms, experiments show our approach notably improves the feb benchmark foot_1 @xcite -a recently pro-posed standardized few-shot self-rationalization benchmark-with 3.4% ∼ 5.1% and 3.0% ∼ 5.8% for task accuracy and the associated explanation metric, respectively. additionally, we validate the approximator's ability with both human and quantitative evaluations. the results suggest our approximator can effectively select plausible explanations that lead to higher accuracy for end-task predictions. in summary, our main contributions are three-fold: ## background and motivation given a trained self-rationalization model f θ (•) and an input sequence x, we denote a prediction f θ (x) = (r, â), where r is the generated free-text rationale and â is the predicted answer, typically a classification label. note that r and â are parsed from the output sequence of f θ (x). evaluation of a self-rationalization model requires assessing both â for the end-task performance and r for the quality of the explanation. with the lack of an ideal and unified automatic metric, the current gold standard for determining the quality of r is to conduct a human evaluation to check its plausibility @xcite @xcite ). an ideal r is considered to be plausible if it is able to justify â, that is, providing a logical and reasonable explanation supporting the model's prediction. however, if r is deemed plausible by humans, it does not mean â is correct. as the example in naturally, generating plausible explanations that can justify the wrong answers should be much harder comparing to justifying the correct answers. since such r usually demonstrates a slight pivot from commonsense yet still introduces a sound reason to support the inaccurate â. we hypothesize this-plausible explanation towards inaccurate endtask prediction-is not the circumstance in most cases of (â, r). in other words, if r is considered to be plausible, it is likely that â is a correct prediction. hence, the first research question arises: rq1: "to what extent do plausible explanations imply correct label predictions?" and if we could verify rq1, the follow-up question would be rq2: "is it possible to automatically identify plausible r and utilize (r, â) for further model improvement?" in the following of our work, we answer rq1 by inspecting the interrelationship between the plausibility of r and the correctness of â (section 4), where we show evidence supporting the linkage to rq2. ergo, we propose zara coupled with selftraining to accomplish rq2 (section 5), improving few-shot self-rationalization models. ## datasets and tasks we adopt feb @xcite , a newly proposed few-shot self-rationalization benchmark, 2021; @xcite only evaluates r with â = a, i.e, explanation for the correctly predicted answer. this may overestimate the quality of explanations @xcite . as the dataset for experiments throughout this work. feb consists of four sub-tasks from existing english-language explainable datasets with free-text explanations: (1) nonsensical sentence selection (comve; @xcite . given two sentences, select the sentence that is less likely to make sense. (2) offensiveness classification (sbic; @xcite . classify a given post as offensive or not. (3) natural language inference (e-snli; @xcite . classify the relationship between two sequences as entailment, neutral, or contradiction. (4) multiple-choice commonsense qa (ecqa; @xcite . given a question, select the correct answer from five choices. the goal for each sub-task is the same, namely, to predict a label for the underling classification task and generate a free-text explanation supporting the model's decision. each sub-task has 60 episodes, and each episode is a train-test split with 48 training examples and 350 evaluation examples. this design of no extra validation data encompasses the flex principles @xcite for performing robust few-shot nlp evaluation to avoid per-episode hyper-parameter tuning, which could inflate the evaluation results considerably as shown in previous work @xcite . hence, a single set of hyper-parameter is used across all episodes. ## correlation between plausibility and correctness as described in section 2, following we attempt to answer rq1 by measuring the correlation between the plausibility of r and the correctness of â. we conduct human studies on results from a self-rationalization model (without self-training) using the feb dataset. we adopt prompt-based finetuning with natural language prompt on a sequenceto-sequence language model to perform few-shot self-rationalization. mapping and example comve example sbic example choice1: i drove my car to the gas station. choice2: i drove my computer to the gas station. answer: choice2 rationale: you can't drive a computer. post: just when i thought women couldn't get any stupider. answer: offensive rationale: this post implies that women are stupid. mapping mapping premise: [rationale] hypothesis: [answer's sentence] nli class: contradiction premise: the post: [post] hypothesis: the post is [answer] because [rationale] nli class: entailment mapped example mapped example premise: you can't drive a computer. hypothesis: i drove my computer to the gas station. premise: the post just when i thought women couldn't get any stupider. hypothesis: the post is offensive because this post implies that women are stupid. e-snli example ecqa example premise: a woman in a black mesh skirt plays acoustic guitar. hypothesis: a woman is wearing black. answer: entailment rationale: the woman is wearing a black mesh, she is wearing black. question: what is a place that has a bench nestled in trees? choices:(a) state park (b) bus stop (c) bus depot (d) statue (e) train station answer: (a) rationale: state park is a protected public garden. public gardens generally have benches for people to sit and relax. gardens are places with lots of trees and plants. mapping mapping premise: [premise] [rationale]. hypothesis: [hypothesis] nli class: [answer] premise: because [rationale] hypothesis: the answer of the question "[question]" is [answer's choice]. nli class: entailment ## zero-shot augmentation of rationale-answer pairs as shown in section 4, plausible explanations imply that the corresponding task predictions are more likely to be correct. following we present zarathe approach towards automatically judging the plausibility of generated explanations, and leverages the high confident rationale-answer pairs to boost model performance via self-training. ## experiments in this section, we discuss the experimental setup and present the results of our proposed method, zara, for improving few-shot self-rationalization via self-training. we also perform human and quantitative evaluations to validate the automatic plausibility assessment for our approximator. ## conclusion in this work, we first show evidences that plausible explanations imply correct label predictions, and leverage a novel nli approximator to automatically identify plausible rationales paired with correct answers from unlabeled results.s by collecting such rationale-answer pairs with self-training, we effectively improve the performance of few-shot self-rationalization for small lms. moreover, we demonstrate the potential for automatic evaluation of free-text explanations. in light of this, we believe developing a supervised approximator with a unified nli mapping schema across tasks to be a promising avenue for future works.