mlsanityData to 14 Oct 2026

How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond

arXiv 2501.05714 ↗First posted 10 Jan 2025cs.CL cs.AI cs.HCcited by 4

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Abstract (arXiv)

With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with humans. This evolution has birthed a novel paradigm in NLP, i.e., human-model cooperation, that has yielded remarkable progress in numerous NLP tasks in recent years. In this paper, we take the first step to present a thorough review of human-model cooperation, exploring its principles, formalizations, and open challenges. In particular, we introduce a new taxonomy that provides a unified perspective to summarize existing approaches. Also, we discuss potential frontier areas and their corresponding challenges. We regard our work as an entry point, paving the way for more breakthrough research in this regard.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 83 of its 181 references that are in the map. First posted on arXiv 10 Jan 2025.

Builds on

83 of 181 references.

References · 83

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2020Learning to summarize from human feedbackcited by 2,588
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  5. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  6. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
  7. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  8. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
  9. 2018CoQA: A Conversational Question Answering Challengecited by 683
  10. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 420
  11. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 297
  12. 2020Consistent Estimators for Learning to Defer to an Expertcited by 271
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  1. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 268
  2. 2017Predict Responsibly: Improving Fairness and Accuracy by Learning to Defercited by 233
  3. 2018Machine Theory of Mindcited by 226
  4. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
  5. 2018To Trust Or Not To Trust A Classifiercited by 207
  6. 2018Decoupling Strategy and Generation in Negotiation Dialoguescited by 162
  7. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  8. 2023Can Large Language Models Infer Causation from Correlation?cited by 146
  9. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  10. 2020Learning to Complement Humanscited by 124
  11. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  12. 2023Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaborationcited by 116
  13. 2021Machine Learning with a Reject Option: A surveycited by 109
  14. 2019The Algorithmic Automation Problem: Prediction, Triage, and Human Effortcited by 101
  15. 2020Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systemscited by 98
  16. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 94
  17. 2019Combating Label Noise in Deep Learning Using Abstentioncited by 86
  18. 2021Towards a Science of Human-AI Decision Making: A Survey of Empirical Studiescited by 86
  19. 2024A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendationscited by 85
  20. 2021Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speechcited by 82
  21. 2020Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistencycited by 78
  22. 2023Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agentscited by 77
  23. 2021Differentiable Learning Under Triagecited by 76
  24. 2024Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agentscited by 74
  25. 2020Interactive Path Reasoning on Graph for Conversational Recommendationcited by 72
  26. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  27. 2023A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospectscited by 66
  28. 2021Editing a classifier by rewriting its prediction rulescited by 65
  29. 2018Direct Uncertainty Prediction for Medical Second Opinionscited by 61
  30. 2020Reinforced active learning for image segmentationcited by 56
  31. 2020STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generationcited by 56
  32. 2022Improving Expert Predictions with Conformal Predictioncited by 54
  33. 2018Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systemscited by 53
  34. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 53
  35. 2022GODEL: Large-Scale Pre-Training for Goal-Directed Dialogcited by 51
  36. 2020Evaluating Conversational Recommender Systems via User Simulationcited by 47
  37. 2022Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairnesscited by 45
  38. 2022PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Financecited by 45

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

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