Venue: NAACL 2024

# XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners

 Yun Luo\({}^{1,3}\) Zhen Yang\({}^{2}\) Fandong Meng\({}^{2}\) Yingjie Li \({}^{1}\) Fang Guo\({}^{1,3}\) Qinglin Qi\({}^{4}\) Jie Zhou\({}^{2}\) Yue Zhang\({}^{1,5}\)\({}^{8}\)

\({}^{1}\)School of Engineering, Westlake University  \({}^{2}\)WeChat AI, Tencent Inc.

\({}^{3}\) Zhejiang University \({}^{4}\) School of Cyber Science and Engineering, Sichuan University

\({}^{5}\) Institute of Advanced Technology, Westlake Institute for Advanced Study

{luoyun,zhangyue}@westlake.edu.cn

###### 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 pretrained 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.

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Footnote †: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6676–6698

June 16-21, 2024 ©2024 Association for Computational Linguistics

## 1 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 Lewis and Catlett (1994); Margatina et al. (2021). 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 Lewis and Catlett (1994); Settles (2009); Zhang et al. (2022b).

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 Lewis (1995); Houlsby et al. (2011); Margatina et al. (2021); Zhang et al. (2022a). However, previous studies have indicated that existing models struggle to accurately quantify predictive uncertainty Guo et al. (2017); Lakshminarayanan et al. (2017), leading to overconfidence and insufficient exploration, i.e., models tend to choose data instances that are uncertain yet repetitively uninformative Margatina et al. (2021). This issue arises because training can lead cross-entropy-based classifiers to learn superficial or spurious patterns Guo et al. (2022, 2023); Srivastava et al. (2020), 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 Frye et al. (1996); Joyce (1999); Rottman and Hastie (2014). 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 1.

Footnote 1: [https://github.com/LuoXiaoHeics/XAL](https://github.com/LuoXiaoHeics/XAL)

## 2 Related Work

**Active Learning** is widely studied in the natural language processing area, ranging from text classification Roy and McCallum (2001); Zhang et al. (2017); Maekawa et al. (2022), and sequence labeling Settles and Craven (2008) to text generation Zhao et al. (2020). Previous methods can be roughly divided into informativeness-based selection strategies, representativeness-based selection strategies, and hybrid selection strategies Zhang et al. (2022a). 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 Guo et al. (2017); Margatina et al. (2021). 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 Roy and McCallum (2001); Margatina et al. (2021).

**Large Language Model**. Recently, LLMs of generative schema have shown excellent performance in various NLP tasks [3]. However, some studies show that in-context learning based on LLMs Radford et al. (2019); Brown et al. (2020) suffers from practical issues such as high computation costs for inference Liu et al. (2022), inclination to their internal knowledge Yan et al. (2024), catastrophic forgetting during instruction

Figure 1: Data selection strategy in AL. Previous work selects the unlabeled data mostly relying on the model’s uncertainly (a), but we propose to further leverage the model’s explanation of its prediction (b).

 

tuning Luo et al. (2023), over-sensitive to example choices and instruction wording Gao et al. (2020); Schick and Schutze (2021). Considering the problems, LLMs are also applied to improve the generalization of smaller models for specific tasks. Some studies distill the knowledge of large language models to a smaller one Hsieh et al. (2023) and some also use LLMs to augment data to achieve stronger text classification performance Ye et al. (2022); Yu et al. (2023). LLMs has also demonstrated a strong capability in generating high-quality reasoning steps Hsieh et al. (2023); Wei et al. (2022); Kojima et al. (2022). In this study we aim to distill the reasoning ability of LLMs to smaller models, and encourage the models to distinguish the reasonablity of explanations to identify informative data in AL scenario.

**Explanation Information**, as external knowledge, has been proven useful for a wide range of tasks in natural language processing Hase and Bansal (2022). Hase et al. (2020) used explanations as additional information and directly fed them into models. Narang et al. (2020) and Shen et al. (2023) took the explanations as outputs and trained NLP models to generate them. How to leverage explanations is still an open problem Hase and Bansal (2022). In the active learning schema, some studies also attempt to leverage the explanations Liang et al. (2020); Wang et al. (2021), but they mainly focus on promoting the generalization abilities of models trained on low-resource data. These AL studies are also hard to implement in text classification tasks and unlike these studies, we explore how to leverage explanations to identify informative unlabeled data for annotation.

## 3 Method

### Overview

Task FormulationWe mainly consider a class text classification task defined on a compact set \({\cal X}\) and a label space \({\cal Y}=\{1,...,C\}\). The data points are sampled i.i.d over the space \({\cal Z}={\cal X}\times{\cal Y}\) as \(\{{\bf x}_{i},y_{i}\}\sim p_{z}\), which can be divided into two sets - the labeled set \(D_{l}\) and the unlabeled set \(D_{u}\). At the beginning of an active learning algorithm, only a small number of data points are randomly selected into the labeled set \(D_{l}\) and we have only access to data points in \(D_{l}\) for training the classification model. Then \(L\) data from \(D_{u}\) are selected for annotation and added to \(D_{l}\) (removed from \(D_{u}\) simultaneously) in \({\cal M}\) multiple rounds.

Model ArchitectureFollowing previous work Devlin et al. (2018), we adopt a pre-trained bi-directional encoder as the backbone for classification. In addition to the encoder, a corresponding uni-directional decoder is applied to generate and score the explanation for the label prediction. During training, we construct \(k\) different explanations \({\textbf{e}}_{r}\), i.e., \(\{{\textbf{e}}_{r}\}_{i},r=0,...,k-1,\) for each example

Figure 2: Our proposed XAL framework, which can be divided into two main parts – the training process (red arrows) and the data selection process (blue arrows). The training process aims to train the encoder-decoder model to learn classification and explanation generation. The data selection process aims to select unlabeled data using predictive entropy and explanation scores.

 

\(\{\mathbf{x}_{i},y_{i}\}\), where \(\textbf{e}_{0}\) is the reasonable explanation and \(\{\textbf{e}_{r>0}\}\) are \(k-1\) unreasonable explanations. We leave the construction process of explanations in Section 3.4 for further descriptions. Before that, we will first present the model training and data selection in Section 3.2 and Section 3.3 respectively. The framework of XAL is shown in Figure 2 and the workflow can be found in Algorithm 1.

### Training

For each text input **x** (we omit all the subscripts of \(i\) for simplicity in this subsection), we first prepend it with a special token \([CLS]\) and then obtain the contextual representation by feeding it into the encoder. The contextual representation of the \(j\)th token is calculated as:

\[\textbf{h}_{j}=Encoder([CLS]+\textbf{x})[j]. \tag{1}\]

The representation for [CLS], i.e., \(\textbf{h}_{0}\) is taken as the sentence representation and fed into the classification layer, which is composed of a linear layer and a softmax function. The probability distribution on label space \(\mathcal{Y}\) can be formulated as:

\[P(y|\textbf{x})=Softmax(Linear(\textbf{h}_{0})). \tag{2}\]

The cross-entropy loss is adopted to optimize the encoder parameters:

\[\mathcal{L}_{cls}=-\sum P(y|\textbf{x})~{}log~{}P(y|\textbf{x}). \tag{3}\]

On the decoder side, the model is trained with teacher forcing to generate the golden explanation \(\textbf{e}_{0}\). The generation loss is calculated as:

\[\mathcal{L}_{gen}=-\sum_{t}log~{}P(\textbf{e}_{0,t}|\textbf{h},\textbf{e}_{0,< t}). \tag{4}\]

To make the decoder a good scorer to rank the reasonable and unreasonable explanations, we additionally adopt a ranking loss to optimize the decoder. In particular, the model is trained to rank between reasonable and unreasonable explanations. The ranking loss can be formulated as:

\[\mathcal{L}_{rank}=\sum_{r>0}max(0,p_{r}-p_{0}), \tag{5}\]

where \(p_{r}\) is the explanation score for \(\textbf{e}_{r}\), calculated as the length-normalized conditional log probability:

\[p_{r}=\frac{\sum_{t}logP(\textbf{e}_{r,t}|\textbf{x},\textbf{e}_{r,<t})}{|| \textbf{e}_{r}||}. \tag{6}\]

The hyper-parameters are adopted to balance the weights of each loss, and the overall loss is formalized as follows:

\[\mathcal{L}=\mathcal{L}_{cls}+\lambda_{1}\mathcal{L}_{gen}+\lambda_{2} \mathcal{L}_{rank}. \tag{7}\]

### Data Selection in AL

After training the model in each iteration, we can obtain an intermediate model \(\pi\). To select the informative data in the unlabeled set \(\mathcal{D}_{u}\), we adopt a combination of the predictive entropy and explanation score. Specifically, for each raw data \(\textbf{x}_{i}\in\mathcal{D}_{u}\), we first generate the explanation \(\textbf{e}_{i}\) by selecting the top-\(1\) output in the beam search. Then, we calculate the explanation score \(p_{i}\) as Eq. 6 and the predictive entropy \(c_{i}\) as Eq. 3. The final score \(s_{i}\) for example \(\textbf{x}_{i}\) is calculated as the weighted sum of the normalized explanation score and predictive entropy:

\[s_{i}=\frac{\lambda}{1+\lambda}\frac{e^{-p_{i}}}{\sum_{i}e^{-p_{i}}}+\frac{1}{ 1+\lambda}\frac{e^{c_{i}}}{\sum_{i}e^{c_{i}}} \tag{8}\]

where the \(\lambda\) is the hyper-parameter to balance the explanation score and the predictive entropy. With the final score for each example, we rank the whole unlabeled instances and select the top \(L\) instances for annotation.

### Generation of Golden Explanations

Previous work has shown that LLMs are good at reasoning Bang et al. (2023); Rajasekharan et al. (2023); Hsieh et al. (2023). Inspired by these studies, we take the LLMs, such as ChatGPT and GPT4, as the teacher models, and query them to generate explanations for each selected labeled data, eliminating the annotation cost of human labor. In particular, we design slightly different prompt templates for different tasks, and the prompt for each task is shown in Appendix A. Taking stance detection as an example, its prompt template is designed as _'The stance of this tweet to the target **[Target]** is **[Label]**, explain the reason within 50 words'_, where the **Target** is the corresponding stance target, and the **Label** is the classification label. The final query to the teacher model is the concatenation of the text and the prompt. We construct a reasonable explanation by feeding the golden label into the query and generate several unreasonable explanations by feeding wrong labels. Figure 7 shows an example that we generate explanations by querying ChatGPT, where we can observe that ChatGPT could provide different explanations according to the given label.

## 4 Experiments

### Tasks and Dataset

We conduct experiments on six different text classification tasks: (1) **Natural Language Inference 

aims to detect whether the meaning of one text is entailed (can be inferred) from the other text; (2) **Paraphrase Detection** requires identifying whether each sequence pair is paraphrased; (3) **Category Sentiment Classification** aims to identify the sentiment (Positive/Negative/Neutral) of a given review to a category of the target such as food and staff; (4) **Stance Detection** aims to identify the stance (Favor/Against/Neutral) of a given text to a target; (5) **Disagreement Detection** aims to detect the stance (Agree/Disagree/Neutral) of one reply to a comment; (6) **Relevance Classification** aims to detect whether a scientific document is relevant to a given topic. The details of the dataset we used are shown in Table 1. Appendix A demonstrates the details and prompts of six datasets with examples. 2

Footnote 2: Without losing generality, we randomly split the training set in RTE, and MRPC into train/dev set with proportion 9:1. In DEBA, we adopt the topic of climate change for experiments.

### Baselines

To demonstrate the effectiveness of our proposed method, we compare XAL with the following nine AL baselines: (1) **Random** uniformly selects unlabeled data for annotation; (2) **Max-Entropy (ME)**[78, 10] calculates the predictive entropy in the current model and selects data with max entropy ; (3) **Bayesian Active Learning by Disagreement (BALD)**[11] exploits the uncertainty of unlabeled data by applying different dropouts at test time; (4) **Breaking Ties (BK)**[10] selects instances with the minimum margin between the top two most likely probabilities ; (5) **Least Confidence (LC)**[12] adopts instances whose most likely label has the least predictive confidence; (6) **Coreset**[101, 102] treats the representations in \(D_{u}\) as cluster centers, and selects the unlabeled data with the most significant distance from its nearest centers; (7) **Batch Active learning by Diverse Gradient Embeddings (BADGE)**[10] measures uncertainty as the gradient magnitude and collects examples where these gradients span a diverse set of directions; (8) **Bayesian Estimate of Mean Proper Scores (BEMPS)**[103] encourages diversity in the vector of expected changes in scores for unlabelled data; (9) **Contrastive Active Learning (CAL)**[10] selects instances with the maximum mean Kullback-Leibler (KL) divergence between its \(m\) nearest neighbors.

## 5 Results and Discussion

### Main Results

We mainly consider two different settings: (1) Given the data selection budget, we observe the trend of changes in model performance; (2) Given the performance upper bound, we observe the number of required instances that the model needs to achieve 90% of the upper-bound performance. We utilize FLAN-T5-large [10] as our backbone network 3, ChatGPT is adopted to generate the explanations. The implemented details can be found in Appendix D and the detailed values of the result can be found in Appendix E.1.

Footnote 3: We implement baselines using FLAN-T5-large as well.

Given Data Selection BudgetFollowing previous work [101, 102], we set the data selection budget as 500 instances and select 100 instances for annotation in each iteration. The results are presented in Figure 3. We can observe that the proposed XAL model consistently outperforms other active learning methods. For instance, in MAMS, our model attains a macroF1 score of 74.04% at th

\begin{table}
\begin{tabular}{l l c c c} \hline Task & Dataset & \# Labels & Train & Dev & Test \\ \hline Natural Language Inference & RTE [100] & 2 & 2,240 & 250 & 278 \\ Paraphrase Detection & MRPC [101] & 2 & 3,667 & 409 & 1,726 \\ Stance Detection & COVID19 [101] & 3 & 4,533 & 800 & 800 \\ Category Sentiment Classification & MAMS [101] & 3 & 7,090 & 888 & 901 \\ (Dis)agreement Detection & DEBA [100] & 3 & 4,617 & 578 & 580 \\ Relevance Classification & CLEF [101] & 2 & 7,847 & 981 & 982 \\ \hline \end{tabular}
\end{table}
Table 1: All the six text classification tasks used in our experiments. The extent of difficulty is roughly in an increasing tendency. RTE and MRPC are fundamental natural language tasks and are included in the widely used benchmark GLUE. MAMS, COVID19, and DEBA require the model to understand the text and give suitable inferences towards a specific target or text, and CLEF further provides a difficult dataset with imbalanced label distribution.

 

higher than the second-best result (LC at 71.83%). Similarly, in DEBA, XAL surpasses the second-best result (BADGE at 58.13%) by 1.78%. These results demonstrate the effectiveness of our XAL framework in addressing text classification tasks.

In COVID19, while the model does not significantly outperform the baselines at the beginning (possibly due to the relatively high complexity of the task), it still exhibits stronger performance with a data count of 300-500, which underscores the effectiveness of the data selection in XAL. In CLEF, we notice that the performance of baseline models is notably unstable due to the significant imbalance in label distribution (the ratio between relevant and irrelevant is approximately 1:21). However, our XAL model achieves superior performance and more consistent improvements over baselines during the data selection process, which validates the effectiveness of XAL, even in challenging scenarios of imbalanced data.

Given Performance Upper BoundIt's also valuable to evaluate the amount of data required for models to achieve comparable performance with those trained on the entire training dataset. Specifically, we begin with an initial labeled set of 100 instances and select a certain number of instances to annotate in each selection iteration 4, and cease the

Figure 4: Experimental results demonstrate how much data, when selected using AL methods, is required for the models to achieve 90% of the performance of those trained on the complete training datasets. In each iteration, we annotate 50 instances. The performance of models trained on the whole training sets is, (a) RTE – \(83.11\%\), (b) MRPC – \(84.74\%\), (c) COVID19 – \(75.45\%\), and (d) DEBA – \(65.71\%\). The green triangles refer to the average values of the experiments on three different initial sets \(D_{l}\) and three different random seeds. The circles refer to outliers. Detailed results can be seen in Appendix E.2.

Figure 3: Results given the data selection budget 500 instances in six text classification tasks, where 100 instances are selected for annotation in each iteration. Here we plot the specific values of XAL and the second significant performance when using 500 instances, and the detailed performance values can be found in Appendix E.1.

 

AL process once the model performance reaches 90% of the upper-bound performance. Experimental results are depicted in Figure 4.5 As observed, XAL requires the least amount of data to reach the performance goal. For instance, in the task of DEBA, XAL necessitates an average of 461.11 data points, which is 55.56 less than the second lowest value (BK-516.67). To conclude, XAL models only require 6%, 3%, 16%, and 10% of the data from RTE, MRPC, COVID19, and DEBA tasks respectively to achieve 90% performance of models that are trained on the entire datasets, which significantly reduces the annotation cost. These results show that the proposed XAL is very cost-efficient in selecting informative unlabeled data.

Footnote 5: For ease of presentation and training efficiency, we only report results on four tasks.

### Ablation Study

We conduct an ablation study to investigate the impact of each module in our model. The results are displayed in Figure 5. Firstly, we conduct a comparison among ME, ME-Exp, and XAL, where ME-Exp has the same model structure as XAL but it selects the unlabeled data with the predicted classification entropy. We observe that ME-Exp can achieve superior performance on most datasets compared to ME, which demonstrates the effectiveness of using explanations. However, XAL further achieves noticeably better performance over ME-Exp, indicating that the improvement in XAL comes not only from the introduction of explanations but also from the data selection strategy (with explanation scores). Next, we compare XAL with a version that removes the ranking loss (_w/o Rank_ in Figure 5). XAL also achieves better performance on most datasets and with different numbers of labeled data, indicating that the ranking loss can enhance the effectiveness of data selection in the AL process. Furthermore, the performance of selecting data solely using the explanation score but without using predictive entropy is also illustrated in Figure 5 (_w/o ME_). We observe that removing ME leads to significant performance drops on most datasets, implying that the predictive entropy and explanation score can complement each other.

To further evaluate how the ranking loss works in XAL, we also compare the model's capability to rank explanations between XAL and its counterpart without ranking loss. Experimental results show that XAL achieves superior performance. For instance, the ranking accuracy in RTE and MRPC for XAL are 73.93% and 78.62%, which are 5.36% and 4.30% higher than those without ranking loss, respectively (detailed results are shown in Appendix E.4). These results suggest that the ranking loss can enhance the model's ability to score the explanations. It is evident that XAL consistently outperforms these alternatives in most time, while there are some fluctuations across different scenarios.

### Explanation Generation

We also carry out experiments to analyze how the generation of explanations impacts model performance. Specifically, we replace ChatGPT with ALPACA-7B [19], and GPT4 6 to generate explanations on the MAMS dataset. The results are presented in Table 2. We also observe that the ALPACA-7B can also provide useful explanations to some extent and enhance the model performance compared with ME through our framework. This suggests that LLMs, when used as an assistant in XAL, can provide consistent explanation generation and enhance model performance. The results also indicate that the model performance can be affected by the explanation generation model and it is applicable to use open-source LLMs. The results of human annotation are also discussed in Appendix F.

Footnote 6: [https://openai.com/gpt-4](https://openai.com/gpt-4)

### Comparison with ChatGPT

We assess ChatGPT on these datasets in both zero-shot and few-shot scenarios, and the outcomes are presented in Table 3. The models, when subjected to supervised fine-tuning with 500 labeled data points in XAL, exhibit either notably improved or comparable performance to ChatGPT across these datasets. This suggests that our model can achieve satisfactory results with minimal cost.

\begin{table}
\begin{tabular}{l c c c c c} \hline  & 100 & 200 & 300 & 400 & 500 \\ \hline ChatGPT & **60.79** & 63.47 & 68.51 & 71.54 & 73.24 \\ ALPACA-7B & 59.52 & 61.75 & 67.77 & 71.12 & 72.24 \\ GPT4 & 59.67 & **64.28** & **69.51** & **72.96** & **74.63** \\ \hline \end{tabular}
\end{table}
Table 2: Model performance on MAMS using different explanation generations. We compare the performance in a certain initial set and random seed.

\begin{table}
\begin{tabular}{c c c c c} \hline  & MRPC & COVID19 & DEBA & CLEF \\ \hline Zero-shot & 72.46 & 66.67 & 48.96 & 34.21 \\ Few-shot & 78.32 & **67.73** & 54.69 & 42.44 \\ Silver & 74.32 & 53.66 & 47.12 & 36.09 \\ XAL (500) & **83.91** & 67.16 & **59.91** & **66.94** \\ \hline \end{tabular}
\end{table}
Table 3: Performance of ChatGPT and performance of models trained on ‘silver’ data.

 

Since XAL queries ChatGPT for generating explanations with extra cost (API calls) to implement AL, we also analyze if we could obtain a better model through querying ChatGPT for more labeled data, i.e. 'silver data' (the labels are probably incorrect). In detail, we use the actively selected data with golden labels, and randomly selected data with 'silver' labels from ChatGPT to train the model in each active iteration. For this process, ChatGPT is employed to annotate a random selection of data from \(D_{u}\), which is three times the size of \(D_{l}\)--mirroring the frequency of queries made for explanations in XAL. We report the final model performance with 500 golden labels and 1500 silver labels in Table 3. We observe that despite the increase in silver data obtained from ChatGPT, our model can still perform more significantly. This outcome is primarily attributed to the uncertain accuracy of ChatGPT's annotations. The lack of reliability in these pseudo labels suggests that merely increasing the quantity of labeled data, without ensuring its quality, may not be an effective strategy for improving model performance.

### Human Evaluation on Interpretability

We evaluate our model's ability to explain its prediction by examining the consistency between the generated explanation and the classification label. Specifically, we randomly select 50 test instances and use the model trained on 500 instances (see Section 5.1) to generate the labels and explanations. Then we ask humans to infer the classification labels based solely on the generated explanations. The consistency is measured by whether the human-inferred label equals the label predicted by the model. We report the consistency rate across all the test sets: MRPC-94%, RTE-94%, COVID19-96%, DEBA-94%, MAMS-94%, CLEF-100%. We find that the consistency rates on all six tasks exceed 94%, which demonstrates that XAL explains its classification prediction very well. Case studies for the generated explanations and the predicted labels are presented in Appendix I.

### Representation Visualization

To understand the potential of XAL in exploring informative unlabeled data, we use t-SNE van der Maaten and Hinton (2008) to "visualize" the data selection procedure of ME and XAL on the task DEBA. Specifically, with the intermediate model in Section 5.1 (trained with 200 labeled instances), 100 instances from the unlabeled set \(D_{u}\) are then selected for annotation. Then, we feed all the labeled and unlabeled instances into the model and get their sentence representations (**h\({}_{0}\)** in Eq. 1). Finally, we apply the t-SNE toolkit to map these representations into a two-dimensional embedding space, which is shown in Figure 6. We can observe that the data are obviously partioned into different clusters, which shows the overconfidence problem in ME, and the unlabeled data selected by ME is only distributed around the decision boundary,

Figure 5: Results of ablation study in the six text classification tasks. We select 100 instances in each iteration and conduct 4 iterations (the same with Section 5.1). The results are measured using macro-F1 scores and they are the average values on three different initial sets \(D_{l}\) and three different random seeds.

 

showing that the model can only select the high-uncertainty data it believes. However, the proposed XAL can select more diverse data, some of which are wrongly classified by the current model. These results demonstrate that the data selection strategy in XAL can identify more informative data and mitigate the problem of overconfidence to some extent. More visualizations are shown in Appendix J.

## 6 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.

## 7 Limitations

XAL takes an initial attempt towards integrating rationales information into active learning. While acknowledging that this approach may necessitate additional computational resources, this augmentation empowers the trained classifier to be both more explainable and more generalized, as the model can generate explanations for its predictions and obtain enhanced performance. Our model, which incorporates a decoder module to obtain the generation score, necessitates more time for data selection, which is detailed in Appendix G, but during the inference, since we use an encoder-decoder models for training, we can directly use the encoder for inference if the explanation generation is not in need. In our experiments, we evaluated our model's effectiveness across six classification tasks in a low-resource setting, but XAL can be used for other tasks with more label classes and industrial downstream applications.

## 8 Ethical Statement

We honor the ACL Code of Ethics. No private data or non-public information was used in this work. All annotators have received labor fees corresponding to the amount of their annotated instances.

## 9 Acknowledgement

We acknowledge financial support of the National Natural Science Foundation of China Key Program under Grant Number 62336006.

## References

* Ash et al. (2019) Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2019. Deep batch active learning by diverse, uncertain gradient lower bounds. In _International Conference on Learning Representations_.
* Bang et al. (2023) Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023. A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. _arXiv preprint arXiv:2302.04023_.
* Bentivogli et al. (2009) Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009. The fifth pascal recognizing textual entailment challenge. _TAC_, 7:8.
* Brown et al. (2020) Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. _Advances in neural information processing systems_, 33:1877-1901.
* Chai et al. (2023) Chengliang Chai, Jiayi Wang, Nan Tang, Ye Yuan, Jiabin Liu, Yuhao Deng, and Guoren Wang. 2023. Efficient coreset selection with cluster-based methods. In _Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_, KDD '23, page 167-178, New York, NY, USA. Association for Computing Machinery.

Figure 6: t-SNE visualizations of contextual representations. To facilitate identification, we outline the areas of labeled data with dashed squares. The colors, i.e. red, blue, and green, refer to the different labels.

 

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022. Scaling instruction-finetuned language models. _arXiv preprint arXiv:2210.11416_.
* Culotta and McCallum (2005) Aron Culotta and Andrew McCallum. 2005. Reducing labeling effort for structured prediction tasks. In _AAAI_, volume 5, pages 746-751.
* Devlin et al. (2018) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. _arXiv preprint arXiv:1810.04805_.
* Dolan et al. (2004) Bill Dolan, Chris Quirk, and Chris Brockett. 2004. Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources. In _COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics_, pages 350-356, Geneva, Switzerland. COLING.
* Frye et al. (1996) Douglas Frye, Philip David Zelazo, Patricia J Brooks, and Mark C Samuels. 1996. Inference and action in early causal reasoning. _Developmental Psychology_, 32(1):120.
* Gao et al. (2020) Tianyu Gao, Adam Fisch, and Danqi Chen. 2020. Making pre-trained language models better few-shot learners. _arXiv preprint arXiv:2012.15723_.
* Gilardi et al. (2023) Fabrizio Gilardi, Meysam Alizadeh, and Mael Kubli. 2023. Chatgpt outperforms crowd-workers for text-annotation tasks. _arXiv preprint arXiv:2303.15056_.
* Glandt et al. (2021) Kyle Glandt, Sarthak Khanal, Yingjie Li, Doina Caragea, and Cornelia Caragea. 2021. Stance detection in COVID-19 tweets. In _Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)_, pages 1596-1611, Online. Association for Computational Linguistics.
* Guo et al. (2017) Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On calibration of modern neural networks. In _International conference on machine learning_, pages 1321-1330. PMLR.
* Guo et al. (2023) Fang Guo, Yun Luo, Linyi Yang, and Yue Zhang. 2023. Scimine: An efficient systematic prioritization model based on richer semantic information. In _Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval_, SIGIR '23, page 205-215, New York, NY, USA. Association for Computing Machinery.
* Guo et al. (2022) Yiduo Guo, Bing Liu, and Dongyan Zhao. 2022. Online continual learning through mutual information maximization. In _Proceedings of the 39th International Conference on Machine Learning_, volume 162 of _Proceedings of Machine Learning Research_, pages 8109-8126. PMLR.
* Hase and Bansal (2022) Peter Hase and Mohit Bansal. 2022. When can models learn from explanations? a formal framework for understanding the roles of explanation data. In _Proceedings of the First Workshop on Learning with Natural Language Supervision_, pages 29-39, Dublin, Ireland. Association for Computational Linguistics.
* Hase et al. (2020) Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal. 2020. Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language? In _Findings of the Association for Computational Linguistics: EMNLP 2020_, pages 4351-4367, Online. Association for Computational Linguistics.
* Houlsby et al. (2011) Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, and Mate Lengyel. 2011. Bayesian active learning for classification and preference learning. _arXiv preprint arXiv:1112.5745_.
* Hsieh et al. (2023) Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes.
* Jiang et al. (2019) Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, and Min Yang. 2019. A challenge dataset and effective models for aspect-based sentiment analysis. In _Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP)_, pages 6280-6285.
* Joyce (1999) James M Joyce. 1999. _The foundations of causal decision theory_. Cambridge University Press.
* Kanoulas et al. (2017) Evangelos Kanoulas, Dan Li, Leif Azzopardi, and Rene Spijker. 2017. Clef 2017 technologically assisted reviews in empirical medicine overview.
* Kingma and Ba (2014) Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. _arXiv preprint arXiv:1412.6980_.
* Kojima et al. (2022) Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. _Advances in neural information processing systems_, 35:22199-22213.
* Lakshminarayanan et al. (2017) Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. _Advances in neural information processing systems_, 30.
* Lewis (1995) David D Lewis. 1995. A sequential algorithm for training text classifiers: Corrigendum and additional data. In _Acm Sigir Forum_, volume 29, pages 13-19. ACM New York, NY, USA.
* Lewis and Catlett (1994) David D Lewis and Jason Catlett. 1994. Heterogeneous uncertainty sampling for supervised learning. In _Machine learning proceedings 1994_, pages 148-156. Elsevier 

Weixin Liang, James Zou, and Zhou Yu. 2020. Alice: Active learning with contrastive natural language explanations. _arXiv preprint arXiv:2009.10259_.
* Liu et al. (2022) Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohhta, Tenghao Huang, Mohit Bansal, and Colin Raffel. 2022. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. _arXiv preprint arXiv:2205.05638_.
* Luo et al. (2023) Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. 2023. An empirical study of catastrophic forgetting in large language models during continual fine-tuning. _arXiv preprint arXiv:2308.08747_.
* Maekawa et al. (2022) Seiji Maekawa, Dan Zhang, Hannah Kim, Sajjadur Rahman, and Estevam Hruschka. 2022. Low-resource interactive active labeling for fine-tuning language models. In _Findings of the Association for Computational Linguistics: EMNLP 2022_, pages 3230-3242.
* Margatina et al. (2021) Katerina Margatina, Giorgos Vernikos, Loic Barrault, and Nikolaos Aletras. 2021. Active learning by acquiring contrastive examples. _arXiv preprint arXiv:2109.03764_.
* Narang et al. (2020) Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020. Wt5?! training text-to-text models to explain their predictions. _arXiv preprint arXiv:2004.14546_.
* Pougue-Biyong et al. (2021) John Pougue-Biyong, Valentina Semenova, Alexandre Matton, Rachel Han, Aerin Kim, Renaud Lambiotte, and Doyne Farmer. 2021. Debagerement: A comment-reply dataset for (dis) agreement detection in online debates. In _Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)_.
* Radford et al. (2019) Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. _OpenAI blog_, 1(8):9.
* Rajasekharan et al. (2023) Abhiramon Rajasekharan, Yankai Zeng, Parth Padalkar, and Gopal Gupta. 2023. Reliable natural language understanding with large language models and answer set programming. _arXiv preprint arXiv:2302.03780_.
* Rottman and Hastie (2014) Benjamin Margolin Rottman and Reid Hastie. 2014. Reasoning about causal relationships: Inferences on causal networks. _Psychological bulletin_, 140(1):109.
* Roy and McCallum (2001) Nicholas Roy and Andrew McCallum. 2001. Toward optimal active learning through monte carlo estimation of error reduction. _ICML, Williamstown_, 2:441-448.
* Scheffer et al. (2001) Tobias Scheffer, Christian Decomain, and Stefan Wrobel. 2001. Active hidden markov models for information extraction. In _International symposium on intelligent data analysis_, pages 309-318. Springer.
* Schick and Schulze (2021) Timo Schick and Hinrich Schutze. 2021. It's not just size that matters: Small language models are also few-shot learners. In _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 2339-2352, Online. Association for Computational Linguistics.
* Schohn and Cohn (2000) Greg Schohn and David Cohn. 2000. Less is more: Active learning with support vector machines. In _Proceedings of the Seventeenth International Conference on Machine Learning_, pages 839-846.
* Schroder et al. (2022) Christopher Schroder, Andreas Niekler, and Martin Potthast. 2022. Revisiting uncertainty-based query strategies for active learning with transformers. In _Findings of the Association for Computational Linguistics: ACL 2022_, pages 2194-2203, Dublin, Ireland. Association for Computational Linguistics.
* Sener and Savarese (2018) Ozan Sener and Silvio Savarese. 2018. Active learning for convolutional neural networks: A core-set approach. In _International Conference on Learning Representations_.
* Settles (2009) Burr Settles. 2009. Active learning literature survey.
* Settles and Craven (2008) Burr Settles and Mark Craven. 2008. An analysis of active learning strategies for sequence labeling tasks. In _Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing_, pages 1070-1079, Honolulu, Hawaii. Association for Computational Linguistics.
* Shen et al. (2023) Jiaming Shen, Jiaul Liu, Dan Finnie, Negar Rahmati, Mike Bendersky, and Marc Najork. 2023. "why is this misleading?": Detecting news headline hallucinations with explanations. In _Proceedings of the ACM Web Conference 2023_, WWW '23, page 1662-1672, New York, NY, USA. Association for Computing Machinery.
* Srivastava et al. (2020) Megha Srivastava, Tatsunori Hashimoto, and Percy Liang. 2020. Robustness to spurious correlations via human annotations. In _International Conference on Machine Learning_, pages 9109-9119. PMLR.
* Tan et al. (2021) Wei Tan, Lan Du, and Wray Buntime. 2021. Diversity enhanced active learning with strictly proper scoring rules. _Advances in Neural Information Processing Systems_, 34:10906-10918.
* Taori et al. (2023) Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsumori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. [https://github.com/tatsu-lab/stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca).
* van der Maaten and Hinton (2008) Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-sne. _Journal of Machine Learning Research_, 9(86):2579-2605.
* Wang et al. (2021) Chaoqi Wang, Adish Singla, and Yuxin Chen. 2021. Teaching an active learner with contrastive examples. _Advances in Neural Information Processing Systems_, 34:17968-17980 

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. _Advances in Neural Information Processing Systems_, 35:24824-24837.
* Yan et al. (2024) Jianhao Yan, Yun Luo, and Yue Zhang. 2024. Refutebench: Evaluating refuting instruction-following for large language models. _arXiv preprint arXiv:2402.13463_.
* Ye et al. (2022) Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong. 2022. Zerogen: Efficient zero-shot learning via dataset generation. _arXiv preprint arXiv:2202.07922_.
* Yu et al. (2023) Yue Yu, Yuchen Zhuang, Rongzhi Zhang, Yu Meng, Jiaming Shen, and Chao Zhang. 2023. Regen: Zero-shot text classification via training data generation with progressive dense retrieval. _arXiv preprint arXiv:2305.10703_.
* Zhang et al. (2022a) Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022a. ALLSH: Active learning guided by local sensitivity and hardness. In _Findings of the Association for Computational Linguistics: NAACL 2022_, pages 1328-1342, Seattle, United States. Association for Computational Linguistics.
* Zhang et al. (2017) Ye Zhang, Matthew Lease, and Byron Wallace. 2017. Active discriminative text representation learning. In _Proceedings of the AAAI Conference on Artificial Intelligence_, volume 31.
* Zhang et al. (2022b) Zhisong Zhang, Emma Strubell, and Eduard Hovy. 2022b. A survey of active learning for natural language processing. In _Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing_, pages 6166-6190, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
* Zhao et al. (2020) Yuekai Zhao, Haoran Zhang, Shuchang Zhou, and Zhihua Zhang. 2020. Active learning approaches to enhancing neural machine translation. In _Findings of the Association for Computational Linguistics: EMNLP 2020_, pages 1796-1806, Online. Association for Computational Linguistics.
* Zhu et al. (2023) Yiming Zhu, Peixian Zhang, Ehsan-Ul Haq, Pan Hui, and Gareth Tyson. 2023. Can chatgpt reproduce human-generated labels? a study of social computing tasks. _arXiv preprint arXiv:2304.10145_.

 

Tasks and Corresponding Prompts

We show the tasks and examples for experiments in Table 4, including natural language inference, paraphrase detection, category sentiment classification, stance detection, (dis)agreement detection, and relevance classification. Then we show how we obtain different explanations in Figure 7 with an example of COVID19. We also show the prompts we used for explanation generation through querying ChatGPT (Table 5).

## Appendix B Explanation Examples

Using the prompts in Appendix A, we show some examples of the obtained explanations in Table 6 by querying ChatGPT.

## Appendix C Algorithm

We show the detailed algorithm of XAL in Algorithm 1.

```
1:Initialization: dataset \(D_{u}\), iteration steps \(\mathcal{M}\), selective number \(L\), training epoch \(\mathcal{T}\).
2:Randomly select \(L\) data from \(D_{u}\), denoted as \(D_{s}\) and remove them in \(D_{u}\).
3:Annotate the data \(\textbf{x}_{i}\in D_{s}\) for \(y^{c}_{i}\) with human annotators.
4:Query ChatGPT for diverse explanations \(y^{g_{r}}_{i}\) for the data \(\{\textbf{x}_{i},y^{c}_{i}\}\in D_{s}\).
5:Add \(\{\textbf{x}_{i},y^{c}_{i},y^{gr}_{i}\}\in D_{s}\) to \(D_{l}\), and empty the set \(D_{s}\).
6:\(m=1\).
7:repeat
8:\(m\gets m+1\)
9: Initialize an explainable classifier \(\pi\) and \(t=0\).
10:repeat
11:\(t\gets t+1\)
12: Calculate optimization loss using data \(\{\textbf{x}_{i},y^{c}_{i},y^{gr}_{i}\}\in D_{l}\).
13: Optimize the explainable classifier \(\pi\).
14:until\(t>\mathcal{T}\)
15:Calculate the predictive entropy \(\textbf{p}_{i}\) and explanation scores \(\textbf{c}_{i}\) of data \(\textbf{x}_{i}\in D_{u}\) using Eq. 6.
16: Calculate the rank score using Eq. 8.
17: Select \(L\) data with the largest score from \(D_{u}\) to \(D_{s}\).
18: Annotate the data in \(D_{u}\) following the steps 3-5.
19:until\(m>\mathcal{M}\)
20:Explainable classifier \(\pi\).
```

**Algorithm 1** Explainable Active Learning Algorithm

### Given upper bound

We show the average number of data required for the model to achieve 90% performance of those trained on all the training data (Table 8).

### Ablation Study

The detailed results of the ablation study are shown in Table 9.

### Capacity of Score

To assess our model's capability to distinguish between reasonable and unreasonable explanations, we evaluate its ranking performance on the test set. Specifically, after four iterations of the AL process as per section 5.1, we prompt ChatGPT to generate diverse explanations for the test data and score them using Eq. 6. In each test step, we feed both 

a reasonable and an unreasonable explanation to our model and calculate the accuracy in predicting the reasonable ones based on the computed explanation score (Table 10). As seen in the results, the model incorporating ranking loss achieves superior performance compared to the model without it. For instance, the accuracy in RTE and MRPC are 73.93% and 78.62% in the model with ranking loss, which are 5.36% and 4.30% higher than those without ranking loss, respectively. The improvement in prediction accuracy suggests that the ranking loss can enhance the model's ability to score the reasonability of explanations.

## Appendix F Human Annotation

We also carry out experiments to analyze how the human generation of explanations impacts model performance. Specifically, we replace ChatGPT with human annotation to generate explanations on the MAMS dataset. For human annotation, three PhD students specializing in NLP annotate the labels and explanations. Specifically, the models achieve the macro-F1 scores of RTE-62.13%, MRPC-63.36%, MAMS-67.38%, COVID19-69.70%, and CLEF-71.56%, which are relatively lower compared to ChatGPT, which could be due to inconsistent annotation styles among annotators and changes in the annotation scheme from the original dataset Gilardi et al. (2023); Zhu et al. (2023). The results also demonstrate the effectiveness of explanation generation through LLMs in XAL.

## Appendix G Cost Analysis

We conduct experiments (on one GPU V100 Tesla) to analyze the time consumption during each data query process in the MAMS task, which involves 7,090 training data instances. The results are as follows: ME-2 minutes, CA-2 minutes, BK-2 minutes, LC-2 minutes, BALD-11 minutes, Coreset-54 minutes, and our model XAL-21 minutes. Upon observation, it's apparent that our model requires more time for querying unlabeled data when compared to methods that leverage model uncertainty. However, it consumes less time than the representativeness-based method Coreset.

## Appendix H Sensitivity Analysis

In this study, we establish our hyper-parameters based on the relative magnitude and importance of various loss functions, consistently applying these across all datasets without resorting to grid search for optimization. This section further explores the sensitivity of our model to these hyper-parameters. Initially, we examine the impact of different \(\lambda_{1}\) values, as depicted in Figure 8. Our observations reveal that the model's performance remains relatively stable with \(\lambda_{1}\) values of 0.3 and 0.1. However, a notable decline in performance occurs when \(\lambda_{1}\) is increased to 0.5, attributed to the generative loss becoming approximately ten times greater than the classification loss. Conversely, reducing \(\lambda_{1}\) to 0.05 results in a significant deterioration in model performance, suggesting that excessively minimizing the generative loss is detrimental. Subsequently, we assess the model's response to various \(\lambda_{2}\) values, detailed in Figure 9. These findings indicate that higher \(\lambda_{2}\) values can adversely affect the model's performance. Yet, the model exhibits lesser sensitivity to changes in \(\lambda_{2}\) when it is equal to or less than 0.01.

## Appendix I Case Study

### Model Generation

Some generation cases are shown (Table 11) from the models trained for 500 data in the AL process

Figure 7: The process to generate diverse explanations from LLMs. We can obtain reasonable and unreasonable explanations by querying ChatGPT with correct and incorrect labels, respectively.

 

of Section 5.1. In these cases, we can find that our model can generate reasonable explanations for the label, which indicates the interpretability of our inference. But in some cases such as the case iv., although the explanation generates the correct label Agree, it explains the label with a wrong reason, which implies that the explainer does not perform perfectly in the small number of data. But it also indicates that we can enhance the model performance in inference and generation by selecting the data with unreasonable explanations through human beings.

### Unreasonable Generation

We also show some cases that our model believes have high unreasonability in the training set (Table 12). It is noted that in these cases the model generates some unreasonable explanations.

## Appendix J Representation Visualization

We further demonstrate more visualizations (Figure 10) in DEBA and Covid19 to show the effectiveness of XAL in exploring informative data.

 



\begin{table}
\begin{tabular}{l l} \hline Task & Text \\ \hline Natural Language Inference & **Sentence 1**: Danny Kennedy, Greenpeace campaigns director, said: “The burden of proof in the Scott Parkin expulsion case lies morally with the Commonwealth, to prove that he is a danger. When the Government brought in anti-terror legislation, they promised the public that these laws would only be used to confront a real and present risk of a terrorist attack, not a sweep-all approach against citizens. Peace is not terrorism. Peace is not a threat to national security. No democratic government should expel a foreign citizen because [it] opposes his political opinions.” \\
**Sentence 2**: Greenpeace director said that peace is terrorism. \\
**Label**: Not Entailment. \\ \hline Paraphrase Detection & **Sentence 1**: Last week the power station’s US owners, AES Corp, walked away from the plant after banks and bondholders refused to accept its financial restructuring offer .", \\  & **Sentence 2**: ”The news comes after Drax’s American owner, AES Corp. AES.N, last week walked away from the plant after banks and bondholders refused to accept its restructuring offer. \\  & **Label**: Paraphrase/Semantic Equivalent. \\ \hline Category Sentiment Classification & **Text: I left feeling unsatisfied, except for having a nice chance to people watch in the cozy atmosphere with my over-priced pasta blogonese. Target: Ambience Label: Positive \\ \hline Stance Detection & **Text:** Michigan is finding individuals 500S for not wearing a mask in public. How do y’ all feel about this? Curious because I am torn about being so forceful but agree that people should wear masks. \#MaskOn. Target: Face Mask Label: Favor \\ \hline (Dis)agreement Detection & **Text 1**: True, but with lower power usage, you have less heat to dissipate, meaning you can overclock it even more. \\  & **Text 2**: AMD creates a chip that saves energy by over 31 times. Someone show this to r/PCMasterRace cause we need to switch to AMD. \\  & **Label**: Agree. \\ \hline Relevance Classification & **Document:** 99mtechnetium penicillin: a renal cortical scanning agent. 99mTcTechnetium penicillamine, a renal cortical imaging agent, can be used to provide a rapid, safe, and non-invasive assessment of renal morphology and the renal vascular supply. Since this agent is not excreted significantly during the imaging procedure cortical scans of high quality can be obtained without image deterioration owing to a superimposed collecting system. These scans, which are clearly superior in anatomical detail to earlier scans using 131I hippuran, can be obtained along with the 131I hippuran renogram when the patient comes to the nuclear medicine department. Herein we demonstrate the anatomical detail it is now possible to achieve by presenting the cortical renal scans and accompanying radiograms from 5 patients with different renal pathology. \\  & **Topic**: Procalcitonin, C-reactive protein, and erythrocyte sedimentation rate for the diagnosis of acute pyelonephritis in children. \\  & **Label**: Not Relevant. \\ \hline \end{tabular}
\end{table}
Table 4: Tasks and examples for experiments.





\begin{table}
\begin{tabular}{l l p{142.3pt}} \hline Task & Prompts & Label set \\ \hline Natural Language Inference & Sentence 1: **\{Text 1**\}. Sentence 2: **\{Text 2**\}. Sentence 1 can **\{Label]** sentence 2, explain the reason within 50 words. & \{Entail, Not Entail\} \\ Paraphrase Detection & Sentence 1: **\{Text 1**\}. Sentence 2: **\{Text 2**\}. The relation between the above two sentences is **\{Label]**, explain the reason within 50 words. & \{Paraphrase Equivalent, Not Paraphrase Equivalent\} \\ Category Sentiment Classification & **\{Text\}.** & \{Positive, Negative, Neutral\} \\ Stance Detection & **\{Text\}. The stance of this tweet to the target **\{Target\}** is **\{Label]**, explain the reason within 50 words. & \{Favor, Neutral, Against\} \\ (Dis)agreement Detection & Sentence 1: **[Text 1]**. Sentence 2: **\{Text 2**\}. The stance of sentence 2 is **\{Label]** to sentence 1, explain the reason within 50 words. & \{Agree, Neutral, Disagree\} \\ Relevance Classification & **\{Document\}. The scientific document is **\{Label]**’ to the research topic **\{Topic\}**, explain the reason within 100 words. & \{Relevant, Not Relevant\} \\ \hline \end{tabular}
\end{table}
Table 5: The prompts we adopted for the tasks in our experiments.





\begin{table}
\begin{tabular}{p{108.4pt} p{256.9pt} p{370.0pt}} \hline Input & Label & Explanation \\ \hline
**_RTE_** & **Not Entailment** & Sentence 1 states that the Nobel prizes are the main forms of recognition for various fields, including peace. However, sentence 2 only mentions that candidates have been chosen for the Nobel Peace Prize, without specifying if they have actually received the prize. Therefore, sentence 2 does not necessarily follow from sentence 1. \\  & Entailment & Sentence 1 can entail sentence 2 because sentence 1 mentions that one of the main forms of recognition for those working for peace is the Nobel prizes. Therefore, it is reasonable to assume that candidates for the Nobel Peace Prize have been chosen. \\ \hline _MRPC_ & **Equiva-Ient** & The two sentences provide similar information but are expressed differently. Sentence 1 mentions that Maddox fell about 10 days ago, while Sentence 2 specifies that the fall occurred earlier this month. Despite the slight difference in wording, both sentences convey the same meaning. \\  & Not Equivalent & The reason why the two sentences are not paraphrases or semantic equivalents is because they provide different information about the timing and reason for Maddox’s rib injury. In the first sentence, it mentions that Maddox fell about 10 days ago while recovering from intestinal surgery, whereas the second sentence states that he fell earlier this month while recovering from surgery without specifying the exact time frame. \\ \hline _COVID19_ & **Favor Tweet:** Placed2nd @AngelaBelcamino Omg people say I have to wear a mask and l’m crystale because I have to wear a mask to save lives boo hoo!! MAGAts are stupid!!! & The stance of this tweet towards face masks is favorable. The reason for this is that the tweet expresses support for wearing masks to save lives and criticizes those who oppose wearing masks, referring to them as ”‘MAGAts” (a term often used to describe supporters of former President Donald Trump). \\ Against & The reason behind the stance against face masks in this tweet seems to be frustration and ridic towards those who oppose wearing masks. The tweet suggests that the person finds the idea of wearing masks to save lives unnecessary and mocks those who support it, particularly referring to them as ’‘‘MAGAts,’’ ‘n derogatory term for supporters of former President Trump. \\ Neutral & The tweet expresses a neutral stance towards face masks by mocking the idea of crying over having to wear one. While it criticizes MAGA supporters, it does not explicitly state a negative or positive opinion about face masks themselves.” \\ \hline \end{tabular}
\end{table}
Table 6: Diverse explanations generated from ChatGPT. The golden label is marked in **bold**.





\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & Random & ME & BALD & BK & LC & Coreset & CAL & BADGE & bMEPS & XAL \\ \hline _RTE_ & & & & & & & & & & \\ \hline

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 \hline \end{tabular}
\end{table}
Table 8: The detailed experimental results about how much data queried by AL methods can the model achieve 90% performance of the models trained on the whole training data. In each iteration, we select 50 data. The model performances trained on the whole training sets are, (a) RTE – 83.11%, (b) MRPC – 84.74%, (c) COVID19 – 75.45%, and (d) DEBA – 65.71%. The green triangles refer to the average values of the nine-times experiments.





\begin{table}
\begin{tabular}{c c c c c c c} \hline  & RTE & MRPC & MAMS & COVID19 & DEBA & CLEF \\ \hline w Ranking Loss & 73.93 & 78.62 & 68.64 & 62.25 & 59.78 & 90.73 \\ w/o Ranking Loss & 68.57 & 74.32 & 62.04 & 58.34 & 55.04 & 87.39 \\ \hline \end{tabular}
\end{table}
Table 10: The performance of scoring the explanations w/o ranking loss.

\begin{table}
\begin{tabular}{c c c c c} \hline ME & ME-Exp & w/o Rank & w/o ME & XAL \\ \hline _RTE_ & & & & \\ \hline

[MISSING_PAGE_POST]

 \hline \end{tabular}
\end{table}
Table 9: Detailed results of ablation study in the six text classification tasks. We select 100 instances in each iteration and conduct 4 iterations. The results are measured using macro-F1 scores and they are the average values on three different initial sets \(D_{l}\) and three different random seeds.





\begin{table}
\begin{tabular}{l l p{142.3pt}} \hline Input & Golden & Explanation \\ \hline i. _RTE_ & Not & Sentence 1 states that Dana Reeve, the widow of Entail- \\
**Sentence 1**: Dana Reeve, the widow of the actor Christopher Reeve, has died of lung cancer at age 44, according to the Christopher Reeve Foundation. & Entailing of 44. It does not provide any information about what caused Christopher Reeve’s accident. Therefore, sentence 1 does not entail sentence 2. \\ \hline ii. _MRPC_ & Not & The reason why the two sentences are not paraphrases or semantic equivalents is because they provide different information. Sentence 1 states that the Hornets were on three-minute alert during the operation, while Sentence 2 states that the security operation was unprecedented. \\ \hline iii. _MAMS_ & Negative & The reason for the negative sentiment towards the staff in this review is because the waiter took the drink order and disappeared after 15 minutes. This indicates a lack of professionalism and attentiveness, which can be frustrating for customers. \\ \hline iv. _COVID19_ & Favor & The stance of this tweet is in favor of face masks. The reason given is that the tweeter believes masks are effective in stopping the spread of disease, and therefore should be encouraged to use them. \\ \hline v. _DEBA_ & Disagree & Sentence 2 disagrees with Sentence 1 because it argues that taking public transportation instead of driving is not feasible for many people living in places with poor public transport provisions or in poorly planned towns. \\ \hline vi. _DEBA_ & Sentence 2 agrees with Sentence 1 because it acknowledges that Biden has made some progress, but it also emphasizes that it is up to activists and local politicians to bring about meaningful change. \\ \hline  & & \\ \end{tabular}
\end{table}
Table 11: Case study of the explanation generation of our model. The model is trained on 500 labeled data instances following the AL process in Section 5.1.





\begin{table}
\begin{tabular}{l l p{142.3pt}} \hline Input & Golden & Explanation \\ \hline i. _MAMS_ & Positive & The reason for the neutral sentiment polarity towards the aspect ”miscellaneous” in the review is that the reviewer prefers the smaller portion size for dinner. This suggests that the overall mining experience is more enjoyable and satisfying, resulting in a neutral sentiment polarity. \\ \hline ii. _MAMS_ & Neutral & The positive sentiment polarity of the review towards the aspect of food is due to the description of the Paella Vallenciana being brought out on hot plates. This suggests that the dish was served fresh and hot, enhancing the dining experience and leaving a positive impression. \\ \hline iii. _COVID9_ & Favor & The tweet expresses a negative stance towards face masks, referring to them as COVID-19. The reason for this stance is not provided within the given word limit. \\ \hline iv. _DEBA_ & Agree & Sentence 2 disagrees with sentence 1 because it suggests that governments have not used taxes for the intended purposes. This implies that they raid the education budget to raise taxes for education, contradicting the claim in sentence 1 that the purpose of a carbon tax is to. \\ \hline  **Sentence 2**: DoE also has the National Renewable Energy Lab, where a lot of the tech development used for renewable energy has happened. The DoE funded the development of fracking. There’s a history of recent appointees using positions there to champion the fossil fuels industry, even if most of what the DoE does is nuclear. \\ \hline v. _DEBA_ & Disagree & Sentence 2 agrees with Sentence 1 because it argues that the person who posts here is not trying to debate, but rather to annoy. It suggests that the solution is to simply out-annoy them and ban them. \\ \end{tabular}
\end{table}
Table 12: Case study of the unreasonable explanation of our model. The model is trained on 500 labeled data instances following the AL process in Section 5.1.



Figure 10: The t-SNE visualization of sentence representations in the data selection process.