# A Survey of Active Learning for Natural Language Processing

arXiv: https://arxiv.org/abs/2210.10109 · HTML: https://mlsanity.com/paper/2210.10109/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 85 of its 296 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 30 of 85)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Competence-based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1903.09848/) (2019)
- [Active Learning by Acquiring Contrastive Examples](https://mlsanity.com/paper/2109.03764/) (2021)
- [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://mlsanity.com/paper/2106.08962/) (2021)
- [Cold-start Active Learning through Self-supervised Language Modeling](https://mlsanity.com/paper/2010.09535/) (2020)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Deep Active Learning for Named Entity Recognition](https://mlsanity.com/paper/1707.05928/) (2017)
- [Discriminative Active Learning](https://mlsanity.com/paper/1907.06347/) (2019)
- [Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study](https://mlsanity.com/paper/1808.05697/) (2018)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Deep Active Learning over the Long Tail](https://mlsanity.com/paper/1711.00941/) (2017)
- [Learning how to Active Learn: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1708.02383/) (2017)
- [Practical Obstacles to Deploying Active Learning](https://mlsanity.com/paper/1807.04801/) (2018)
- [Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering](https://mlsanity.com/paper/2107.02331/) (2021)
- [Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers](https://mlsanity.com/paper/2107.05687/) (2021)
- [SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup](https://mlsanity.com/paper/2010.02322/) (2020)
- [A Survey of Active Learning for Text Classification using Deep Neural Networks](https://mlsanity.com/paper/2008.07267/) (2020)
- [Predicting Performance for Natural Language Processing Tasks](https://mlsanity.com/paper/2005.00870/) (2020)
- [Putting Humans in the Natural Language Processing Loop: A Survey](https://mlsanity.com/paper/2103.04044/) (2021)
- [Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates](https://mlsanity.com/paper/2101.08133/) (2021)
- [Low-resource Deep Entity Resolution with Transfer and Active Learning](https://mlsanity.com/paper/1906.08042/) (2019)

## Built on by (most cited first, 30 of 40)

- [Active Learning Principles for In-Context Learning with Large Language Models](https://mlsanity.com/paper/2305.14264/) (2023)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Feedback Efficient Online Fine-Tuning of Diffusion Models](https://mlsanity.com/paper/2402.16359/) (2024)
- [Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection](https://mlsanity.com/paper/2310.20046/) (2023)
- [VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool](https://mlsanity.com/paper/2407.05355/) (2024)
- [Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost](https://mlsanity.com/paper/2306.15766/) (2023)
- [ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios](https://mlsanity.com/paper/2405.10808/) (2024)
- [On the Limitations of Large Language Models (LLMs): False Attribution](https://mlsanity.com/paper/2404.04631/) (2024)
- [Learning Hidden Markov Models Using Conditional Samples](https://mlsanity.com/paper/2302.14753/) (2023)
- [D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias](https://mlsanity.com/paper/2305.17013/) (2023)
- [On the Limitations of Simulating Active Learning](https://mlsanity.com/paper/2305.13342/) (2023)
- [D2PO: Discriminator-Guided DPO with Response Evaluation Models](https://mlsanity.com/paper/2405.01511/) (2024)
- [Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems](https://mlsanity.com/paper/2402.16751/) (2024)
- [Investigating Multi-source Active Learning for Natural Language Inference](https://mlsanity.com/paper/2302.06976/) (2023)
- [An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets](https://mlsanity.com/paper/2306.08954/) (2023)
- [Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision](https://mlsanity.com/paper/2310.20153/) (2023)
- [DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation](https://mlsanity.com/paper/2606.28725/) (2026)
- [Active Continual Learning: On Balancing Knowledge Retention and Learnability](https://mlsanity.com/paper/2305.03923/) (2023)
- [Active Learning for Natural Language Generation](https://mlsanity.com/paper/2305.15040/) (2023)
- [Transferring Procedural Knowledge across Commonsense Tasks](https://mlsanity.com/paper/2304.13867/) (2023)
- [Active Learning for NLP with Large Language Models](https://mlsanity.com/paper/2401.07367/) (2024)
- [Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Tasks](https://mlsanity.com/paper/2407.03895/) (2024)
- [Interactive Machine Teaching by Labeling Rules and Instances](https://mlsanity.com/paper/2409.05199/) (2024)
- [DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning](https://mlsanity.com/paper/2502.00305/) (2025)
- [PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learning](https://mlsanity.com/paper/2507.16424/) (2025)
- [MAViC: Multimodal Active Learning for Video Captioning](https://mlsanity.com/paper/2212.11109/) (2022)
- [XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners](https://mlsanity.com/paper/2310.05502/) (2023)
- [CALICO: Confident Active Learning with Integrated Calibration](https://mlsanity.com/paper/2407.02335/) (2024)
- [A Survey of the State-of-the-Art in Conversational Question Answering Systems](https://mlsanity.com/paper/2509.05716/) (2025)
- [Active Learning for Decision Trees with Provable Guarantees](https://mlsanity.com/paper/2601.20775/) (2026)

## Abstract (arXiv)

In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.