Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023)

Abstract: Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts’ real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.

## introduction state-of-the-art (sota) language models @xcite @xcite figure 1 : our dual-model al system architecture at every iteration: 1) the al data selector chooses a few unlabeled examples; 2) human annotators provide an explanation and label for each data instance; 3) the annotated explanations are used to finetune the explanationgeneration model; 4) the annotated labels and generated explanations are used to finetune the prediction model. then, humans can review the predicted labels and generated explanations for unlabeled data and start the next iteration. green arrows indicate the training target. the lack of explainability and untrustworthiness of models is magnified in the real world @xcite , where domain experts rarely only annotate a decision label in their daily workflow without providing explanations (i.e., clinical diagnoses by clinicians) @xcite , and humans need explanations to understand and trust model predictions @xcite . therefore, a few approaches were proposed to retrospectively analyze the probability distribution within the model or ask models to generate explanations along with predictions @xcite @xcite @xcite , despite, the former is still very difficult for laymen to understand while the latter explanations are not faithful toward predictions. as researchers looked into the quality @xcite of humanannotated natural language explanations @xcite @xcite , they discovered numerous issues in existing datasets @xcite @xcite , such that the human annotations are of low quality and significant inconsistency. furthermore, the ever-increasing costs in terms of labor, finances, and time for large-scale, high-quality data annotations remain a persistent challenge for the research community. this challenge has given rise to various methodologies to reduce reliance on human annotations, such as active learning (al) @xcite . al is a human-in-the-loop framework that utilizes al sampling strategies to iteratively select a small number of representative examples, request oracle annotations, and subsequently fine-tune the model using the annotated data. however, prior al works predominantly focus on labels and overlook the fact that real-world scenarios often need both labels and natural language explanations. in this work, we propose a dual-model al architecture for human annotation of labels and explanations, drawing inspiration from the human decision-making process. our system consists of: we integrate al to reduce human annotation efforts and establish human trustworthiness by actively engaging humans in the training process. we design a novel data diversity-based al sampling strategy to select the most representative examples by exploiting the explanation annotations, which is analogous to the prevalent core-set @xcite strategy. our al architecture aims to support low-resource model predictions and ai trustworthiness by explicitly generating natural language explanations. specifically, we request label and free-form explanation annotations for a very limited number of examples (e.g., 3 or 10) selected by our al sampling strategy at every al iteration. subsequently, the generated explanations serve as input for the final prediction, demonstrating the potential for these explanations to support the model's predictions faithfully. we conduct two al simulations with different amounts of samplings and iterations on a largescale nli dataset with human-annotated explanations to justify incorporating explanations in al data selection can consistently outperform random, traditional data diversity-based, and model probability-based sampling strategies. we make the code publically available foot_0 . a human evaluation of perceived validity, explainability, and preference of the generated explanations among our system, a sota explanationgeneration system, and human-annotated explanations shows that, despite human explanations being ranked highest, explanations generated by our system are preferred over the sota system. additionally, we conduct three ablation studies to explore the capability and potential of our proposed al architecture in transfer learning, generalizability, and incorporating large language models (llms) for explanation generation to further reduce human efforts. llms demonstrate exceptional explanationgeneration capabilities on relatively simple tasks. however, their effectiveness in handling complex real-world tasks warrants in-depth study. in this paper, we leverage the e-snli dataset as the benchmark dataset for our al simulation experiment because 1) the classification task is popular and representative, 2) the massive data size ensures data diversity, and 3) explanations for a classification task may provide more effective help compared to cqa task where training and testing data may be unrelated. we additionally conduct an ablation study on the ecqa dataset to explore the generalizability of our proposed al architecture. ## evaluation we conduct the al simulation experiment with the e-snli @xcite dataset. the primary objective is to justify that our proposed dualmodel framework, when combined with humanannotated explanations in al data selection, can effectively identify more representative and helpful data from a reasonably large-scale dataset. given that e-snli dataset comprises a substantial 549, 367 examples in the train split, we performed a preliminary experiment to determine a reasonable number of candidate data for the al simulation. this approach aims to save time and computing resources. our goal is to identify an ideal candidate data size that would not introduce potentially biased feature distributions or significantly degrade model performance when compared to fine-tuning on the full dataset. we employ the pre-trained t5-base @xcite as the backbone for all the experiments and provide the hyperparameters in appendix c. ## conclusion and future work in summary, this paper introduces a novel dualmodel al system designed to address the common real-world need for domain experts to provide both classification labels and natural language explanations. our system comprises a purpose-built data diversity-based al example selector and two sequence-to-sequence language models, one for explanation generation and the other for label prediction. through an al simulation evaluation and a human assessment of the e-snli dataset, our results demonstrate the effectiveness of explanations in al sampling with our system. they consistently outperform both baselines, and the explanations generated by our system are preferred over a stateof-the-art explanation-generation system. our work lays a step-stone towards a humancentered interactive ai solution (it can be easily implemented as an interactive system as illustrated in fig 7 in appendix d) that supports domain experts for their data annotation tasks. many realworld tasks still require domain experts to review and annotate each data instance with a decision and an explanation for accountability purposes (e.g., a lawyer reviewing and signing off on a legal document). we invite fellow researchers to join us in advancing this research direction, essential for supporting this prevalent real-world requirement.