mlsanityData to 7 Oct 2026

Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents

arXiv 2402.09205 ↗First posted 14 Feb 2024cs.CL cs.AI cs.HCcited by 71

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adept at devising strategies and performing tasks, these agents struggle with seeking clarification and grasping precise user intentions. To bridge this gap, we introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users' implicit intentions through explicit queries. Next, we propose the incorporation of model experts as the upstream in agent designs to enhance user-agent interaction. Employing IN3, we empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires user intentions, and refines them into actionable goals before starting downstream agent task execution. Integrating it into the XAgent framework, we comprehensively evaluate the enhanced agent system regarding user instruction understanding and execution, revealing that our approach notably excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. All the data and codes are released.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 30 of its 73 references that are in the map. First posted on arXiv 14 Feb 2024.

Builds on

30 of 73 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub28 Dec
  2. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
  3. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  4. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug

Other references · 26

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  6. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  7. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  8. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  9. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  10. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  11. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
  12. 2023Reasoning with Language Model is Planning with World Modelcited by 846
14 more
  1. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  2. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 659
  3. 2022Visual Programming: Compositional visual reasoning without trainingcited by 507
  4. 2023Large Language Models as Tool Makerscited by 293
  5. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 136
  6. 2023GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Informationcited by 115
  7. 2023Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Modelscited by 92
  8. 2023Wider and Deeper LLM Networks are Fairer LLM Evaluatorscited by 91
  9. 2018Analyzing and Characterizing User Intent in Information-seeking Conversationscited by 36
  10. 2022Learning to Execute Actions or Ask Clarification Questionscited by 30
  11. 2023Toolink: Linking Toolkit Creation and Using through Chain-of-Solving on Open-Source Modelcited by 29
  12. 2022Human-Centric Research for NLP: Towards a Definition and Guiding Questionscited by 9
  13. 2022Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoningcited by 6
  14. 2014Bayes and Naive Bayes Classifiercited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 12; 2025: 35; 2026: 24.

71 papers.

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  1. 2024Aligning to Thousands of Preferences via System Message Generalizationcited by 84
  2. 2024Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistancecited by 84
  3. 2025UserBench: An Interactive Gym Environment for User-Centric Agentscited by 69
  4. 2024Ask-before-Plan: Proactive Language Agents for Real-World Planningcited by 59
  5. 2025SMART: Self-Aware Agent for Tool Overuse Mitigationcited by 52
  6. 2024Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrievalcited by 50
  7. 2024AGILE: A Novel Reinforcement Learning Framework of LLM Agentscited by 45
  8. 2025VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applicationscited by 45
  9. 2025AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuningcited by 44
  10. 2025ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestrationcited by 44
  11. 2024Learning to Ask: When LLM Agents Meet Unclear Instructioncited by 38
  12. 2024ShortcutsBench: A Large-Scale Real-world Benchmark for API-based Agentscited by 36
38 more
  1. 2025From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Userscited by 32
  2. 2025UserRL: Training Interactive User-Centric Agent via Reinforcement Learningcited by 27
  3. 2024What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Usecited by 24
  4. 2025CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactionscited by 20
  5. 2025FingerTip 20K: A Benchmark for Proactive and Personalized Mobile LLM Agentscited by 19
  6. 2026AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Conditioncited by 19
  7. 2024A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluationscited by 16
  8. 2025CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answeringcited by 15
  9. 2025CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent Systemcited by 15
  10. 2025AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysiscited by 13
  11. 2025AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarificationcited by 12
  12. 2025ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mindcited by 12
  13. 2025InteractComp: Evaluating Search Agents With Ambiguous Queriescited by 12
  14. 2026When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarificationcited by 12
  15. 2025Do not Abstain! Identify and Solve the Uncertaintycited by 11
  16. 2025Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generationcited by 10
  17. 2025Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Abilitycited by 10
  18. 2025A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directionscited by 9
  19. 2025TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Taskscited by 9
  20. 2025ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agentscited by 9
  21. 2024Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimizationcited by 8
  22. 2025LLAMAPIE: Proactive In-Ear Conversation Assistantscited by 8
  23. 2026InfoPO: Information-Driven Policy Optimization for User-Centric Agentscited by 8
  24. 2025RECAP: REwriting Conversations for Intent Understanding in Agentic Planningcited by 7
  25. 2025ToolSpectrum : Towards Personalized Tool Utilization for Large Language Modelscited by 6
  26. 2026ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devicescited by 5
  27. 2024Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Taskscited by 4
  28. 2025How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyondcited by 4
  29. 2026ProactBench: Beyond What The User Asked Forcited by 4
  30. 2024Human-Centered LLM-Agent User Interface: A Position Papercited by 3
  31. 2025GraphicBench: A Planning Benchmark for Graphic Design with Language Agentscited by 3
  32. 2025ALLOY: Generating Reusable Agent Workflows from User Demonstrationcited by 3
  33. 2026Egocentric Co-Pilot: Web-Native Smart-Glasses Agents for Assistive Egocentric AIcited by 3
  34. 2026Architecting Trust in Artificial Epistemic Agentscited by 3
  35. 2026Interactive Evaluation Requires a Design Sciencecited by 3
  36. 2025Improving Language Models with Intentional Analysiscited by 2
  37. 2025LLM-Driven Preference Data Synthesis for Proactive Prediction of the Next User Utterance in Human-Machine Dialoguecited by 2
  38. 2026Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?cited by 2

50 most cited of 71 All 71 (JSON)

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  4. 2025Quick on the Uptake: Eliciting Implicit Intents from Human Demonstrations for Personalized Mobile-Use Agents
  5. 2026π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows
  6. 2025AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents
  7. 2025Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition
  8. 2025Agent-Initiated Interaction in Phone UI Automation