mlsanityData to 14 Oct 2026

Interactive Natural Language Processing

arXiv 2305.13246 ↗First posted 22 May 2023cs.CL cs.AIcited by 31

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Abstract (arXiv)

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence. This paradigm considers language models as agents capable of observing, acting, and receiving feedback iteratively from external entities. Specifically, language models in this context can: (1) interact with humans for better understanding and addressing user needs, personalizing responses, aligning with human values, and improving the overall user experience; (2) interact with knowledge bases for enriching language representations with factual knowledge, enhancing the contextual relevance of responses, and dynamically leveraging external information to generate more accurate and informed responses; (3) interact with models and tools for effectively decomposing and addressing complex tasks, leveraging specialized expertise for specific subtasks, and fostering the simulation of social behaviors; and (4) interact with environments for learning grounded representations of language, and effectively tackling embodied tasks such as reasoning, planning, and decision-making in response to environmental observations. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept. We then provide a systematic classification of iNLP, dissecting its various components, including interactive objects, interaction interfaces, and interaction methods. We proceed to delve into the evaluation methodologies used in the field, explore its diverse applications, scrutinize its ethical and safety issues, and discuss prospective research directions. This survey serves as an entry point for researchers who are interested in this rapidly evolving area and offers a broad view of the current landscape and future trajectory of iNLP.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 178 of its 701 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

178 of 701 references.

References · 178

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  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  8. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
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38 more
  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  3. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  4. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
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  6. 2023A Survey of Large Language Modelscited by 2,576
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  18. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  19. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
  20. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
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  22. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
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Citing papers per year: 2023: 15; 2024: 9; 2025: 5; 2026: 2.

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  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  2. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 222
  3. 2023RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Modelscited by 175
  4. 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Taskscited by 168
  5. 2023Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arenacited by 78
  6. 2023RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Modelscited by 65
  7. 2023TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usagecited by 55
  8. 2025MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Modelscited by 48
  9. 2023LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programscited by 45
  10. 2023LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzlescited by 41
  11. 2024MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memorycited by 35
  12. 2023How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advancescited by 33
19 more
  1. 2025From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Userscited by 32
  2. 2024Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Biascited by 20
  3. 2023Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Modelscited by 19
  4. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 18
  5. 2024MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Modelscited by 17
  6. 2026Latent Space Communication via K-V Cache Alignmentcited by 17
  7. 2023Rethinking Large Language Models in Mental Health Applicationscited by 16
  8. 2023Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Costcited by 12
  9. 2024Grounding Language Models for Visual Entity Recognitioncited by 12
  10. 2023Towards A Unified View of Answer Calibration for Multi-Step Reasoningcited by 8
  11. 2024Read to Play (R2-Play): Decision Transformer with Multimodal Game Instructioncited by 4
  12. 2024Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Gamecited by 3
  13. 2023LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Modelscited by 2
  14. 2024Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Modelscited by 2
  15. 2025Iterative Resolution of Prompt Ambiguities Using a Progressive Cutting-Search Approachcited by 2
  16. 2026Pause or Fabricate? Training Language Models for Grounded Reasoningcited by 2
  17. 2025Exploring a Large Language Model for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Developmentcited by 1
  18. 2024"What's my model inside of?": Exploring the role of environments for grounded natural language understandingcited by 0
  19. 2025Towards Efficient and Effective Alignment of Large Language Modelscited by 0