{"total":144,"rows":[{"id":"2209.01975","title":"Selective Annotation Makes Language Models Better Few-Shot Learners","created":"2022-09-05","cited_by_count":243,"area":null},{"id":"2204.08499","title":"DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning","created":"2022-04-18","cited_by_count":147,"area":null},{"id":"2302.13539","title":"Finding Support Examples for In-Context Learning","created":"2023-02-27","cited_by_count":104,"area":null},{"id":"2303.04947","title":"InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning","created":"2023-03-08","cited_by_count":90,"area":null},{"id":"2310.19596","title":"LLMaAA: Making Large Language Models as Active Annotators","created":"2023-10-30","cited_by_count":79,"area":null},{"id":"2209.00099","title":"Efficient Methods for Natural Language Processing: A Survey","created":"2022-08-31","cited_by_count":78,"area":null},{"id":"2306.05175","title":"Large-scale Dataset Pruning with Dynamic Uncertainty","created":"2023-06-08","cited_by_count":63,"area":null},{"id":"2305.14264","title":"Active Learning Principles for In-Context Learning with Large Language Models","created":"2023-05-23","cited_by_count":54,"area":null},{"id":"2408.15204","title":"Can Unconfident LLM Annotations Be Used for Confident Conclusions?","created":"2024-08-27","cited_by_count":49,"area":null},{"id":"2308.10524","title":"Dataset Quantization","created":"2023-08-21","cited_by_count":48,"area":null},{"id":"2306.04459","title":"Uncertainty in Natural Language Processing: Sources, Quantification, and Applications","created":"2023-06-05","cited_by_count":44,"area":null},{"id":"2210.10109","title":"A Survey of Active Learning for Natural Language Processing","created":"2022-10-18","cited_by_count":40,"area":null},{"id":"2311.13613","title":"Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning","created":"2023-11-22","cited_by_count":38,"area":null},{"id":"2310.20046","title":"Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection","created":"2023-10-30","cited_by_count":37,"area":null},{"id":"2303.04449","title":"Loss-Curvature Matching for Dataset Selection and Condensation","created":"2023-03-08","cited_by_count":33,"area":null},{"id":"2505.17799","title":"A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances","created":"2025-05-23","cited_by_count":28,"area":null},{"id":"2203.12918","title":"A Rationale-Centric Framework for Human-in-the-loop Machine Learning","created":"2022-03-24","cited_by_count":27,"area":null},{"id":"2109.04282","title":"Cartography Active Learning","created":"2021-09-09","cited_by_count":26,"area":null},{"id":"2312.00678","title":"The Efficiency Spectrum of Large Language Models: An Algorithmic Survey","created":"2023-12-01","cited_by_count":26,"area":null},{"id":"2302.05096","title":"Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information","created":"2023-02-10","cited_by_count":24,"area":null},{"id":"2205.04980","title":"ALLSH: Active Learning Guided by Local Sensitivity and Hardness","created":"2022-05-10","cited_by_count":23,"area":null},{"id":"2211.08358","title":"MEAL: Stable and Active Learning for Few-Shot Prompting","created":"2022-11-15","cited_by_count":20,"area":null},{"id":"2310.14019","title":"You Only Condense Once: Two Rules for Pruning Condensed Datasets","created":"2023-10-21","cited_by_count":20,"area":null},{"id":"2405.00705","title":"SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning","created":"2024-04-23","cited_by_count":19,"area":null},{"id":"2209.06995","title":"Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach","created":"2022-09-15","cited_by_count":16,"area":null},{"id":"2212.05974","title":"Federated Few-Shot Learning for Mobile NLP","created":"2022-12-12","cited_by_count":15,"area":null},{"id":"2305.13016","title":"Iterative Forward Tuning Boosts In-Context Learning in Language Models","created":"2023-05-22","cited_by_count":15,"area":null},{"id":"2312.10897","title":"Generalized Category Discovery with Large Language Models in the Loop","created":"2023-12-18","cited_by_count":15,"area":null},{"id":"2203.09735","title":"PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning","created":"2022-03-18","cited_by_count":14,"area":null},{"id":"2306.10087","title":"ActiveGLAE: A Benchmark for Deep Active Learning with Transformers","created":"2023-06-16","cited_by_count":14,"area":null},{"id":"2311.15614","title":"FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models","created":"2023-11-27","cited_by_count":14,"area":null},{"id":"2311.09782","title":"More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering","created":"2023-11-16","cited_by_count":13,"area":null},{"id":"2407.07268","title":"Dataset Quantization with Active Learning based Adaptive Sampling","created":"2024-07-09","cited_by_count":13,"area":null},{"id":"2305.17013","title":"D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias","created":"2023-05-26","cited_by_count":12,"area":null},{"id":"2204.09347","title":"Active Few-Shot Learning with FASL","created":"2022-04-20","cited_by_count":11,"area":null},{"id":"2302.06960","title":"Data pruning and neural scaling laws: fundamental limitations of score-based algorithms","created":"2023-02-14","cited_by_count":11,"area":null},{"id":"2402.17316","title":"Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation","created":"2024-02-27","cited_by_count":11,"area":null},{"id":"2406.04273","title":"ELFS: Label-Free Coreset Selection with Proxy Training Dynamics","created":"2024-06-06","cited_by_count":10,"area":null},{"id":"2410.11114","title":"Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios","created":"2024-10-14","cited_by_count":10,"area":null},{"id":"2112.07522","title":"LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework","created":"2021-12-14","cited_by_count":9,"area":null},{"id":"2305.13342","title":"On the Limitations of Simulating Active Learning","created":"2023-05-21","cited_by_count":9,"area":null},{"id":"2306.08306","title":"Towards Balanced Active Learning for Multimodal Classification","created":"2023-06-14","cited_by_count":9,"area":null},{"id":"2310.08923","title":"Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning","created":"2023-10-13","cited_by_count":9,"area":null},{"id":"2409.01081","title":"Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization","created":"2024-09-02","cited_by_count":9,"area":null},{"id":"2202.08316","title":"FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction","created":"2022-02-16","cited_by_count":8,"area":null},{"id":"2411.15763","title":"Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation","created":"2024-11-24","cited_by_count":8,"area":null},{"id":"2402.13741","title":"Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction","created":"2024-02-21","cited_by_count":7,"area":null},{"id":"2406.05677","title":"Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification","created":"2024-06-09","cited_by_count":7,"area":null},{"id":"2406.17312","title":"Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning","created":"2024-06-25","cited_by_count":7,"area":null},{"id":"2503.00828","title":"Training-Free Dataset Pruning for Instance Segmentation","created":"2025-03-02","cited_by_count":7,"area":null},{"id":"2302.06976","title":"Investigating Multi-source Active Learning for Natural Language Inference","created":"2023-02-14","cited_by_count":6,"area":null},{"id":"2410.04275","title":"Language Model-Driven Data Pruning Enables Efficient Active Learning","created":"2024-10-05","cited_by_count":6,"area":null},{"id":"2204.04711","title":"Data Augmentation for Biomedical Factoid Question Answering","created":"2022-04-10","cited_by_count":5,"area":null},{"id":"2208.08749","title":"Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training","created":"2022-08-18","cited_by_count":5,"area":null},{"id":"2305.14576","title":"Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings","created":"2023-05-23","cited_by_count":5,"area":null},{"id":"2306.08313","title":"A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks","created":"2023-06-14","cited_by_count":5,"area":null},{"id":"2401.16193","title":"Contributing Dimension Structure of Deep Feature for Coreset Selection","created":"2024-01-29","cited_by_count":5,"area":null},{"id":"2402.03038","title":"Automatic Combination of Sample Selection Strategies for Few-Shot Learning","created":"2024-02-05","cited_by_count":5,"area":null},{"id":"2404.01588","title":"Hallucination Diversity-Aware Active Learning for Text Summarization","created":"2024-04-02","cited_by_count":5,"area":null},{"id":"2404.05623","title":"AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets","created":"2024-04-08","cited_by_count":5,"area":null},{"id":"2411.15796","title":"Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning","created":"2024-11-24","cited_by_count":5,"area":null},{"id":"2505.10736","title":"Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization","created":"2025-05-15","cited_by_count":5,"area":null},{"id":"2206.13749","title":"Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction","created":"2022-06-28","cited_by_count":4,"area":null},{"id":"2209.10193","title":"Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning","created":"2022-09-21","cited_by_count":4,"area":null},{"id":"2212.11680","title":"Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis","created":"2022-12-20","cited_by_count":4,"area":null},{"id":"2310.20153","title":"Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision","created":"2023-10-31","cited_by_count":4,"area":null},{"id":"2311.08811","title":"Correlation-aware active learning for surgery video segmentation","created":"2023-11-15","cited_by_count":4,"area":null},{"id":"2407.18058","title":"I can listen but cannot read: An evaluation of two-tower multimodal systems for instrument recognition","created":"2024-07-25","cited_by_count":4,"area":null},{"id":"2408.09709","title":"Dataset Distillation for Histopathology Image Classification","created":"2024-08-19","cited_by_count":4,"area":null},{"id":"2510.00303","title":"Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detection","created":"2025-09-30","cited_by_count":4,"area":null},{"id":"2112.02611","title":"Multi-View Active Learning for Short Text Classification in User-Generated Data","created":"2021-12-05","cited_by_count":3,"area":null},{"id":"2301.01531","title":"MoBYv2AL: Self-supervised Active Learning for Image Classification","created":"2023-01-04","cited_by_count":3,"area":null},{"id":"2305.15040","title":"Active Learning for Natural Language Generation","created":"2023-05-24","cited_by_count":3,"area":null},{"id":"2307.00968","title":"REAL: A Representative Error-Driven Approach for Active Learning","created":"2023-07-03","cited_by_count":3,"area":null},{"id":"2405.01373","title":"ATOM: Attention Mixer for Efficient Dataset Distillation","created":"2024-05-02","cited_by_count":3,"area":null},{"id":"2408.04482","title":"SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios","created":"2024-08-08","cited_by_count":3,"area":null},{"id":"2409.17385","title":"Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Prediction","created":"2024-09-25","cited_by_count":3,"area":null},{"id":"2503.00867","title":"DUAL: Diversity and Uncertainty Active Learning for Text Summarization","created":"2025-03-02","cited_by_count":3,"area":null},{"id":"2505.17228","title":"Automated Capability Evaluation of Foundation Models","created":"2025-05-22","cited_by_count":3,"area":null},{"id":"2508.02313","title":"NMS: Efficient Edge DNN Training via Near-Memory Sampling on Manifolds","created":"2025-08-04","cited_by_count":3,"area":null},{"id":"2508.20230","title":"Coresets from Trajectories: Selecting Data via Correlation of Loss Differences","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2510.20673","title":"Efficient Multi-bit Quantization Network Training via Weight Bias Correction and Bit-wise Coreset Sampling","created":"2025-10-23","cited_by_count":3,"area":null},{"id":"2605.23482","title":"Multimodal Distribution Matching for Vision-Language Dataset Distillation","created":"2026-05-22","cited_by_count":3,"area":null},{"id":"2211.14912","title":"Impact of Strategic Sampling and Supervision Policies on Semi-supervised Learning","created":"2022-11-27","cited_by_count":2,"area":null},{"id":"2302.07260","title":"Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks","created":"2023-02-14","cited_by_count":2,"area":null},{"id":"2306.07215","title":"Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection","created":"2023-06-12","cited_by_count":2,"area":null},{"id":"2310.00283","title":"Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition","created":"2023-09-30","cited_by_count":2,"area":null},{"id":"2402.00219","title":"FedCore: Straggler-Free Federated Learning with Distributed Coresets","created":"2024-01-31","cited_by_count":2,"area":null},{"id":"2402.05356","title":"Exploring Learning Complexity for Efficient Downstream Dataset Pruning","created":"2024-02-08","cited_by_count":2,"area":null},{"id":"2403.15744","title":"On the Fragility of Active Learners for Text Classification","created":"2024-03-23","cited_by_count":2,"area":null},{"id":"2409.05199","title":"Interactive Machine Teaching by Labeling Rules and Instances","created":"2024-09-08","cited_by_count":2,"area":null},{"id":"2410.19631","title":"Efficient Biological Data Acquisition through Inference Set Design","created":"2024-10-25","cited_by_count":2,"area":null},{"id":"2411.04637","title":"Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop","created":"2024-11-07","cited_by_count":2,"area":null},{"id":"2502.00305","title":"DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning","created":"2025-02-01","cited_by_count":2,"area":null},{"id":"2503.09701","title":"Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2506.08070","title":"Info-Coevolution: An Efficient Framework for Data Model Coevolution","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2507.16424","title":"PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learning","created":"2025-07-22","cited_by_count":2,"area":null},{"id":"2507.17768","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","created":"2025-07-17","cited_by_count":2,"area":null},{"id":"2604.12342","title":"CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training","created":"2026-04-14","cited_by_count":2,"area":null},{"id":"2606.08574","title":"OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework","created":"2026-06-07","cited_by_count":2,"area":null}],"next_cursor":"100"}