XAL : EX plainable Active Learning Makes Classifiers Better Low-resource Learners (2024)

Abstract: Active learning (AL), which aims to construct an effective training set by iteratively curating the most formative unlabeled data for annotation, has been widely used in low-resource tasks. Most active learning techniques in classification rely on the model’s uncertainty or disagreement to choose unlabeled data, suffering from the problem of over-confidence in superficial patterns and a lack of exploration.Inspired by the cognitive processes in which humans deduce and predict through causal information, we take an initial attempt towards integrating rationales into AL and propose a novel Explainable Active Learning framework (XAL) for low-resource text classification, which aims to encourage classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Specifically, besides using a pre-trained bi-directional encoder for classification, we employ a pre-trained uni-directional decoder to generate and score the explanation. We further facilitate the alignment of the model with human reasoning preference through a proposed ranking loss. During the selection of unlabeled data, the predicted uncertainty of the encoder and the explanation score of the decoder complement each other as the final metric to acquire informative data. Extensive experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines. Analysis indicates that the proposed method can generate corresponding explanations for its predictions.

## introduction active learning (al) is a machine-learning paradigm that efficiently acquires data for annotation from a (typically large) unlabeled data pool and iteratively trains models @xcite . al frameworks have attracted considerable attention from researchers due to their high realistic values reduce the data annotation costs by concentrating the human labeling effort on the most informative data points, which can be applied in low-resources tasks @xcite @xcite . most previous al methods rely on model predictive uncertainty or disagreement for unlabeled data, and the most uncertain data are believed to be the most informative and worthful ones to be annotated @xcite @xcite . however, previous studies have indicated that existing models struggle to accurately quantify predictive uncertainty @xcite , leading to overconfidence and insufficient exploration, i.e., models tend to choose data instances that are uncertain yet repetitively uninformative @xcite . this issue arises because training can lead cross-entropy-based classifiers to learn superficial or spurious patterns @xcite @xcite , rather than the causal information between inputs and labels. in the context of cognitive science and psychological science, humans make decisions or inferences by exploring causal information @xcite @xcite . for example, when learning to differentiate animals, humans do not merely rely on statistical features such as colors or feathers. they also consider the creatures' habits, such as dietary patterns, and kinship, such as the species of the parents, to engage in exploring rationales, thereby determining the species of the organism. intuitively, explanations of the rational can help the model confirm whether it understands how to make classifications, and explaining the reasons behind the classification also enhances the justification of the inference confidence. it motivates us to encourage classifiers to learn the rationales behind inferences and explore unlabeled data for which the model cannot provide reasonable explanations. in doing so, the model can learn rationales between labels and texts and reduce reliance on superficial patterns, which leads to improved generalization and more effective exploration within al frameworks. the intuition is illustrated in figure 1 . given the above observations, we introduce an explainable active learning framework (xal) for text classification tasks. this framework consists of two main components: the training process and the data selection process. primarily, we adopt a pre-trained bi-directional encoder for classification and a pre-trained uni-directional decoder to generate and score explanations that serve as expressions of rationales in human language. in the training phase, we use the classification labels and explanations to optimize the model parameters. besides, to further enhance the decoder's ability to score explanations, we design a ranking loss that optimizes the model to differentiate between reasonable and unreasonable explanations. to implement this ranking loss, we generate a variety of explanations (both reasonable and unreasonable) for labeled data by querying chatgpt with different prompts, thereby eliminating the need for additional human annotation effort. subsequently, during the data selection phase, we amalgamate the predictive uncertainty of the encoder and the explanation score of the decoder to rank unlabeled data. the most informative data are then annotated and incorporated into further training. we conduct experiments on various text classification tasks involving different level of difficulty in understanding rationals. experimental results manifest that xal can achieve substantial improvement in all tasks. ablation studies demonstrate the effectiveness of each component, and human evaluation shows that the model trained in xal works well in explaining its prediction. xal also demonstrates superior performance with only 500 instances when compared to in-context learning by chatgpt, underscoring the effectiveness of our model at a minimal cost. to our knowledge, we are the first to incorporate the model's explanation (explanation score) to improve the effectiveness of data selection in al process. the codes and data have been released in the link to facilitate further research foot_0 . ## related work active learning is widely studied in the natural language processing area, ranging from text classification @xcite @xcite , and sequence labeling @xcite ) to text generation @xcite . previous methods can be roughly divided into informativeness-based selection strategies, representativeness-based selection strategies, and hybrid selection strategies @xcite . the most mainstream methods, i.e., informativeness-based methods, are mostly characterized using model uncertainty, disagreement, or performance prediction, which suffers from over-confidence and a lack of exploration @xcite . on the other hand, the representativeness-based methods rely on model inputs such as the representations of texts, which tends to select simple data samples and results in unsatisfactory performance @xcite . ## conclusion in this paper, we proposed a novel explainable active learning (xal) framework for text classification. experiments demonstrated that xal achieves substantial improvements compared with previous al methods. further analysis indicated that the proposed method can generate corresponding explanations for its predictions.