Papers with active learning strategies
ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios (2026.tacl-1)
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| Challenge: | Active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. |
| Approach: | They propose an active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. |
| Outcome: | The proposed approach outperforms existing methods ADAPET, PERFECT, and SetFit in few-shot scenarios and can be extended to non-few scenarios. |
Active learning for deep semantic parsing (P18-2)
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| Challenge: | Existing methods for generating training data for semantic parsing are slow and expensive. |
| Approach: | They propose active learning for "overnight" and "natural language" parsing with a logical form . they propose several active learning strategies for overnight data collection . |
| Outcome: | The proposed approach reduces the cost of training data for deep parsing tasks by reducing the number of crowd workers required. |
ALAMBIC : Active Learning Automation Methods to Battle Inefficient Curation (2023.eacl-demo)
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| Challenge: | ALAMBIC is an open-source web-based platform for annotating text data through active learning for classification task. |
| Approach: | They present an open-source web-based platform for annotating text data through active learning for classification task. |
| Outcome: | The proposed model can be downloaded and used in downstream tasks and integrates with other types of models, features and active learning strategies. |
Reducing cohort bias in natural language understanding systems with targeted self-training scheme (2023.acl-industry)
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| Challenge: | In deep learning models, it is hard to capture all the variations of the language that different users can use. |
| Approach: | They propose a framework that uses four active learning strategies to identify important samples coming from new users and a self training phase where a teacher model is trained from the first phase to expand the training data with relevant cohort utterances. |
| Outcome: | The proposed framework reduces the bias related to new customers in a digital voice assistant system by using two phases: a fixing phase and a self training phase. |
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)
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| Challenge: | Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon. |
| Approach: | They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function. |
| Outcome: | The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks. |
DeMuX: Data-efficient Multilingual Learning (2024.naacl-long)
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| Challenge: | Pre-trained multilingual models have enabled deployment of NLP technologies for multiple languages, but their performance under an annotation budget remains an open question. |
| Approach: | They propose a framework that prescribes the exact data-points to label from vast amounts of unlabelled multilingual data, having unknown degrees of overlap with the target set. |
| Outcome: | The proposed framework outperforms strong baselines in 84% of the test cases in the zero-shot setting of disjoint source and target language sets. |
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets (2024.naacl-long)
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| Challenge: | Standard pool-based active learning is computationally expensive on large pools and often reaches low accuracy by overfitting the initial decision boundary. |
| Approach: | They propose a pool-based active learning method that selects class-specific instances from a labelled set and retrieves the most similar unlabelled instances from the pool. |
| Outcome: | Experiments with AnchorAL show that it is faster, often reducing runtime from hours to minutes, and trains more performant models. |
ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly used to solve the entity recognition task. |
| Approach: | They propose a framework to select the most informative and representative samples for LLM in-context learning. |
| Outcome: | The proposed framework outperforms baselines on three specialized domain datasets. |