Papers with uncertainty sampling
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. |
CAL-Log: Cost-Aware Active Learning with Logarithmic Cognitive Effort Modeling and Online Adaptation to Human Annotation Behavior (2026.acl-srw)
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| Challenge: | Standard uncertainty sampling assumes that annotating a 500-word document requires the same effort as a 50-word tweet, leading to suboptimal resource allocation when documents vary in length. |
| Approach: | They propose a cost-aware AL variant using logarithmic cost modeling where C(x) is the predicted annotation time for document x and L(x), is its token length. |
| Outcome: | Experiments on ten text classification benchmarks show a 3.3 speedup over BADGE and 3.9 over Entropy sampling to reach F1=0.80, with large effect sizes. |
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation (2025.coling-main)
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| Challenge: | Active learning (AL) techniques reduce labeling costs for training neural machine translation models by selecting smaller representative subsets from unlabeled data for annotation. |
| Approach: | They propose an AL strategy that combines uncertainty and diversity for sentence selection. |
| Outcome: | The proposed method prioritizes diverse instances having high model uncertainty for annotation in early iterations. |
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study (D18-1)
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| Challenge: | Existing studies on Active Learning (AL) for natural language processing have limited data requirements. |
| Approach: | They propose a Bayesian active learning approach that reduces deep learning's data dependence by comparing models and acquisition functions. |
| Outcome: | The proposed approach outperforms i.i.d. baselines and is more efficient than other approaches. |
Active Learning Principles for In-Context Learning with Large Language Models (2023.findings-emnlp)
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| Challenge: | In-context learning has significantly enhanced predictive performance in few-shot learning settings. |
| Approach: | They propose to use pool-based Active Learning to identify the most informative demonstrations for few-shot learning over a single iteration to identify best demonstrations. |
| Outcome: | The proposed model outperforms all other methods, including random sampling, in the analysis of 24 classification and multi-choice tasks. |
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)
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| Challenge: | Existing uncertainty sampling methods are time-consuming and can't be executed frequently. |
| Approach: | They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials. |
| Outcome: | The proposed approach outperforms baselines on effectiveness on five datasets. |
RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings (2026.findings-acl)
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| Challenge: | Experimental evaluations show that RL methods favor outliers rather than truly informative samples under low-resource and class-imbalanced conditions. |
| Approach: | They propose a robust sample selection strategy using reinforcement learning to identify the most informative samples using a class imbalance approach. |
| Outcome: | The proposed strategy improves model transferability while maintaining robust performance under extreme class imbalance compared to traditional methods. |
Cold-start Active Learning through Self-supervised Language Modeling (2020.emnlp-main)
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| Challenge: | Labeling data is a fundamental bottleneck in machine learning due to annotation cost and time. |
| Approach: | They propose a strategy that uses the pre-training loss to find examples that surprise the model and minimize labeling costs. |
| Outcome: | The proposed approach reduces labeling costs and costs by using pre-trained language models. |
CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (2024.emnlp-main)
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Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis
| Challenge: | In-context learning (ICL) uses few-shot labeled examples to perform selective annotation. |
| Approach: | They propose an algorithm that incorporates uncertainty sampling into selective annotation for ICL . CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples . |
| Outcome: | The proposed algorithm outperforms existing methods for low-budget active learning (AL) it is up to 2x more budget-efficient than SOTA methods for high-budge AL. |