mlsanityData to 7 Oct 2026

From Selection to Generation: A Survey of LLM-based Active Learning

arXiv 2502.11767 ↗First posted 17 Feb 2025cs.LG cs.CLcited by 18

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Abstract (arXiv)

Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) have been employed not only for selection but also for generating entirely new data instances and providing more cost-effective annotations. Motivated by the increasing importance of high-quality data and efficient model training in the era of LLMs, we present a comprehensive survey on LLM-based Active Learning. We introduce an intuitive taxonomy that categorizes these techniques and discuss the transformative roles LLMs can play in the active learning loop. We further examine the impact of AL on LLM learning paradigms and its applications across various domains. Finally, we identify open challenges and propose future research directions. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques and deploy them to new applications.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 45 of its 88 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

45 of 88 references.

References · 45

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  3. 2017Deep Bayesian Active Learning with Image Datacited by 812
  4. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  5. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  6. 2020A Survey of Deep Active Learningcited by 547
  7. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  8. 2017Six Challenges for Neural Machine Translationcited by 334
  9. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
  10. 2023PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Promptscited by 218
  11. 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustmentcited by 171
  12. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
33 more
  1. 2020Contextual Diversity for Active Learningcited by 139
  2. 2021Batch Active Learning at Scalecited by 127
  3. 2020UnQovering Stereotyping Biases via Underspecified Questionscited by 124
  4. 2024How to Train Data-Efficient LLMscited by 91
  5. 2021Gone Fishing: Neural Active Learning with Fisher Embeddingscited by 88
  6. 2022A Comparative Survey of Deep Active Learningcited by 88
  7. 2017Deep Active Learning over the Long Tailcited by 82
  8. 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 78
  9. 2019This Email Could Save Your Life: Introducing the Task of Email Subject Line Generationcited by 58
  10. 2020A Survey of Active Learning for Text Classification using Deep Neural Networkscited by 50
  11. 2024ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agentscited by 35
  12. 2023Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Costcited by 33
  13. 2023Active Preference Inference using Language Models and Probabilistic Reasoningcited by 29
  14. 2024Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Banditscited by 26
  15. 2020Active Learning: Problem Settings and Recent Developmentscited by 23
  16. 2024Enhancing Text Classification through LLM-Driven Active Learning and Human Annotationcited by 20
  17. 2024Multi-Objective Alignment of Large Language Models Through Hypervolume Maximizationcited by 20
  18. 2024ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarioscited by 17
  19. 2024SelectLLM: Can LLMs Select Important Instructions to Annotate?cited by 16
  20. 2024A Comprehensive Survey on Retrieval Methods in Recommender Systemscited by 16
  21. 2024Causal-Guided Active Learning for Debiasing Large Language Modelscited by 15
  22. 2024Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scalecited by 14
  23. 2023Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Taskscited by 13
  24. 2024ELAD: Explanation-Guided Large Language Models Active Distillationcited by 11
  25. 2023Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecturecited by 10
  26. 2020Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Disseminationcited by 9
  27. 2021Hitting the Target: Stopping Active Learning at the Cost-Based Optimumcited by 9
  28. 2024Language Model-Driven Data Pruning Enables Efficient Active Learningcited by 6
  29. 2024Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learningcited by 5
  30. 2023Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervisioncited by 4
  31. 2024Can Active Label Correction Improve LLM-based Modular AI Systems?cited by 4
  32. 2024APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matchingcited by 3
  33. 2024Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Explorationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 12.

18 papers.

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  1. 2025CTRLS: Chain-of-Thought Reasoning via Latent State-Transitioncited by 13
  2. 2026Skill-R1: Agent Skill Evolution via Reinforcement Learningcited by 12
  3. 2025Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendationscited by 10
  4. 2026Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learningcited by 8
  5. 2026ProbeLLM: Automating Principled Diagnosis of LLM Failurescited by 7
  6. 2026Principle-Evolvable Scientific Discovery via Uncertainty Minimizationcited by 6
  7. 2026Uncertainty-Aware Clarification in LLM Agents with Information Gaincited by 6
  8. 2025The Curious Language Model: Strategic Test-Time Information Acquisitioncited by 5
  9. 2025Training-Free Active Learning Framework in Materials Science with Large Language Modelscited by 1
  10. 2026Luna-2: Scalable Single-Token Evaluation with Small Language Modelscited by 1
  11. 2025MEASER: Malware embedding attacks on open-source LLMscited by 0
  12. 2025The Silent Scholar Problem: A Probabilistic Framework for Breaking Epistemic Asymmetry in LLM Agentscited by 0
6 more
  1. 2026Distilling LLM Reasoning into Graph of Concept Predictorscited by 0
  2. 2026Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognitioncited by 0
  3. 2026Improving Selective Classification with Pairwise Queries for Binary Classificationcited by 0
  4. 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalancecited by 0
  5. 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0
  6. 2026LLMs as Acquisition Policies for Finite-Pool Materials Optimization: A Controlled Studycited by 0