mlsanityData to 7 Oct 2026

Putting Humans in the Natural Language Processing Loop: A Survey

arXiv 2103.04044 ↗First posted 6 Mar 2021cs.CL cs.AI cs.HCcited by 40

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Abstract (arXiv)

How can we design Natural Language Processing (NLP) systems that learn from human feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks that continuously integrate human feedback to improve the model itself. HITL NLP research is nascent but multifarious -- solving various NLP problems, collecting diverse feedback from different people, and applying different methods to learn from collected feedback. We present a survey of HITL NLP work from both Machine Learning (ML) and Human-Computer Interaction (HCI) communities that highlights its short yet inspiring history, and thoroughly summarize recent frameworks focusing on their tasks, goals, human interactions, and feedback learning methods. Finally, we discuss future directions for integrating human feedback in the NLP development loop.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 6 Mar 2021.

Builds on

14 of 32 references.

References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Learning to summarize from human feedbackcited by 2,562
  3. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  4. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 428
  5. 2018Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Informationcited by 110
  6. 2019Learning from Dialogue after Deployment: Feed Yourself, Chatbot!cited by 102
  7. 2018Can Neural Machine Translation be Improved with User Feedback?cited by 71
  8. 2018Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systemscited by 53
  9. 2016Dialogue Learning With Human-In-The-Loopcited by 46
  10. 2019Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Modelscited by 8
  11. 2014ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problemscited by 7
  12. 2019TopicSifter: Interactive Search Space Reduction Through Targeted Topic Modelingcited by 5
2 more
  1. 2018Counterfactual Learning from Human Proofreading Feedback for Semantic Parsingcited by 4
  2. 2016Interactive Semantic Featuring for Text Classificationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 4; 2023: 12; 2024: 12; 2025: 3; 2026: 1.

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  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  2. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  3. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  4. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
  5. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  6. 2023Auditing large language models: a three-layered approachcited by 135
  7. 2021What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpuscited by 79
  8. 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
  9. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
  10. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  11. 2024Large Language Model-based Human-Agent Collaboration for Complex Task Solvingcited by 38
  12. 2023Interactive Natural Language Processingcited by 31
28 more
  1. 2021Dodrio: Exploring Transformer Models with Interactive Visualizationcited by 28
  2. 2021Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updatescited by 27
  3. 2023Aligning Language Models with Human Preferences via a Bayesian Approachcited by 27
  4. 2022ProtoTEx: Explaining Model Decisions with Prototype Tensorscited by 21
  5. 2023Interactive Concept Learning for Uncovering Latent Themes in Large Text Collectionscited by 15
  6. 2022Human-Centric Research for NLP: Towards a Definition and Guiding Questionscited by 9
  7. 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
  8. 2023IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Modelscited by 8
  9. 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactionscited by 8
  10. 2024KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Promptscited by 7
  11. 2024iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summariescited by 6
  12. 2021Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Taskscited by 4
  13. 2025How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyondcited by 4
  14. 2026Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agentscited by 4
  15. 2022On the Security Vulnerabilities of Text-to-SQL Modelscited by 2
  16. 2023Translate your gibberish: black-box adversarial attack on machine translation systemscited by 2
  17. 2024Facilitating Opinion Diversity through Hybrid NLP Approachescited by 2
  18. 2024Hands-On Tutorial: Labeling with LLM and Human-in-the-Loopcited by 2
  19. 2023NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Toolscited by 1
  20. 2023Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memorycited by 1
  21. 2024A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)cited by 1
  22. 2024Uncovering Biases with Reflective Large Language Modelscited by 1
  23. 2024Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Scriptcited by 1
  24. 2021Topic-time Heatmaps for Human-in-the-loop Topic Detection and Trackingcited by 0
  25. 2023Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loopcited by 0
  26. 2024An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotationscited by 0
  27. 2024Human-Centric NLP or AI-Centric Illusion?: A Critical Investigationcited by 0
  28. 2025Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionalscited by 0