# ALICE: Active Learning with Contrastive Natural Language Explanations

arXiv: https://arxiv.org/abs/2009.10259 · HTML: https://mlsanity.com/paper/2009.10259/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 30 of 36)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts](https://mlsanity.com/paper/2202.06523/) (2022)
- [When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data](https://mlsanity.com/paper/2102.02201/) (2021)
- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Leveraging Language to Learn Program Abstractions and Search Heuristics](https://mlsanity.com/paper/2106.11053/) (2021)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [VideoCon: Robust Video-Language Alignment via Contrast Captions](https://mlsanity.com/paper/2311.10111/) (2023)
- [Supervising Model Attention with Human Explanations for Robust Natural Language Inference](https://mlsanity.com/paper/2104.08142/) (2021)
- [A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification](https://mlsanity.com/paper/2109.11301/) (2021)
- ["Why is this misleading?": Detecting News Headline Hallucinations with Explanations](https://mlsanity.com/paper/2302.05852/) (2023)
- [GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering](https://mlsanity.com/paper/2104.10283/) (2021)
- [Rich Semantics Improve Few-shot Learning](https://mlsanity.com/paper/2104.12709/) (2021)
- [Teaching an Active Learner with Contrastive Examples](https://mlsanity.com/paper/2110.14888/) (2021)
- [VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives](https://mlsanity.com/paper/2206.11212/) (2022)
- [Using Natural Language Explanations to Rescale Human Judgments](https://mlsanity.com/paper/2305.14770/) (2023)
- [Predictive Chemistry Augmented with Text Retrieval](https://mlsanity.com/paper/2312.04881/) (2023)
- [HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations](https://mlsanity.com/paper/2106.00162/) (2021)
- [LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework](https://mlsanity.com/paper/2112.07522/) (2021)
- [Mediators: Conversational Agents Explaining NLP Model Behavior](https://mlsanity.com/paper/2206.06029/) (2022)
- [Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions](https://mlsanity.com/paper/2207.03133/) (2022)
- [Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering](https://mlsanity.com/paper/2305.08135/) (2023)
- [May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability](https://mlsanity.com/paper/2309.13965/) (2023)
- [Compositional Mixture Representations for Vision and Text](https://mlsanity.com/paper/2206.06404/) (2022)
- [SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalization](https://mlsanity.com/paper/2202.13100/) (2022)
- [How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning](https://mlsanity.com/paper/2308.08224/) (2023)
- [Formal Models of Active Learning from Contrastive Examples](https://mlsanity.com/paper/2506.15893/) (2025)
- [Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2](https://mlsanity.com/paper/2506.22444/) (2025)
- [Improved Robust Algorithms for Learning with Discriminative Feature Feedback](https://mlsanity.com/paper/2209.03753/) (2022)
- [XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners](https://mlsanity.com/paper/2310.05502/) (2023)
- [Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes](https://mlsanity.com/paper/2311.15946/) (2023)

## Abstract (arXiv)

Training a supervised neural network classifier typically requires many annotated training samples. Collecting and annotating a large number of data points are costly and sometimes even infeasible. Traditional annotation process uses a low-bandwidth human-machine communication interface: classification labels, each of which only provides several bits of information. We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning. ALICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations from experts. Then it extracts knowledge from these explanations using a semantic parser. Finally, it incorporates the extracted knowledge through dynamically changing the learning model's structure. We applied ALICE in two visual recognition tasks, bird species classification and social relationship classification. We found by incorporating contrastive explanations, our models outperform baseline models that are trained with 40-100% more training data. We found that adding 1 explanation leads to similar performance gain as adding 13-30 labeled training data points.