mlsanityData to 7 Oct 2026

Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction

arXiv 2312.03022 ↗First posted 5 Dec 2023cs.AI cs.CL cs.LGcited by 5

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

This paper introduces CooperKGC, a novel framework challenging the conventional solitary approach of large language models (LLMs) in knowledge graph construction (KGC). CooperKGC establishes a collaborative processing network, assembling a team capable of concurrently addressing entity, relation, and event extraction tasks. Experimentation demonstrates that fostering collaboration within CooperKGC enhances knowledge selection, correction, and aggregation capabilities across multiple rounds of interactions.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 40 of its 75 references that are in the map. First posted on arXiv 5 Dec 2023.

Builds on

40 of 75 references.

References · 40

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
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  8. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
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  10. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
  11. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  12. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
28 more
  1. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  2. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 599
  3. 2019Explain Yourself! Leveraging Language Models for Commonsense Reasoningcited by 367
  4. 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 356
  5. 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
  6. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  7. 2023GPT-NER: Named Entity Recognition via Large Language Modelscited by 158
  8. 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 155
  9. 2023ExpertPrompting: Instructing Large Language Models to be Distinguished Expertscited by 140
  10. 2021Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extractioncited by 129
  11. 2023PRD: Peer Rank and Discussion Improve Large Language Model based Evaluationscited by 114
  12. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunitiescited by 97
  13. 2023MetaAgents: Large Language Model Based Agents for Decision-Making on Teamingcited by 85
  14. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
  15. 2023Exploring the Feasibility of ChatGPT for Event Extractioncited by 76
  16. 2021Re-TACRED: Addressing Shortcomings of the TACRED Datasetcited by 73
  17. 2023GPT-RE: In-context Learning for Relation Extraction using Large Language Modelscited by 62
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  21. 2022Generative Knowledge Graph Construction: A Reviewcited by 49
  22. 2023Making Large Language Models Perform Better in Knowledge Graph Completioncited by 48
  23. 2023ChatLLM Network: More brains, More intelligencecited by 37
  24. 2023Revisiting Large Language Models as Zero-shot Relation Extractorscited by 29
  25. 2023Guideline Learning for In-context Information Extractioncited by 24
  26. 2021End-to-End NLP Knowledge Graph Constructioncited by 21
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  28. 2022Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?cited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 1.

5 papers.

  1. 2024Chain of Agents: Large Language Models Collaborating on Long-Context Taskscited by 201
  2. 2024Automated Construction of Theme-specific Knowledge Graphscited by 15
  3. 2024Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorizationcited by 15
  4. 2024AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extractioncited by 6
  5. 2026From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
  2. 2025LLM-empowered knowledge graph construction: A survey
  3. 2025Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning
  4. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativity
  5. 2025AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities
  6. 2026GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
  7. 2024Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective
  8. 2026RACER: Reinforced Agent Collaboration for Explainable Reasoning on Knowledge Graphs