mlsanityData to 14 Oct 2026

Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources

arXiv 2305.13269 ↗First posted 22 May 2023cs.CLcited by 122

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Cited by 39 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present chain-of-knowledge (CoK), a novel framework that augments large language models (LLMs) by dynamically incorporating grounding information from heterogeneous sources. It results in more factual rationales and reduced hallucination in generation. Specifically, CoK consists of three stages: reasoning preparation, dynamic knowledge adapting, and answer consolidation. Given a knowledge-intensive question, CoK first prepares several preliminary rationales and answers while identifying the relevant knowledge domains. If there is no majority consensus among the answers from samples, CoK corrects the rationales step by step by adapting knowledge from the identified domains. These corrected rationales can plausibly serve as a better foundation for the final answer consolidation. Unlike prior studies that primarily use unstructured data, CoK also leverages structured knowledge sources such as Wikidata and tables that provide more reliable factual information. To access both unstructured and structured knowledge sources in the dynamic knowledge adapting stage, we propose an adaptive query generator that allows the generation of queries for various types of query languages, including SPARQL, SQL, and natural sentences. Moreover, to minimize error propagation between rationales, CoK corrects the rationales progressively using preceding corrected rationales to generate and correct subsequent rationales. Extensive experiments show that CoK consistently improves the performance of LLMs on knowledge-intensive tasks across different domains.

Key facts. Cited by 122 papers in the map (data through 14 Oct 2026). Builds on 35 of its 53 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

35 of 53 references.

References · 35

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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23 more
  1. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  4. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,919
  5. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
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  16. 2022Is GPT-3 a Good Data Annotator?cited by 208
  17. 2021FeTaQA: Free-form Table Question Answeringcited by 175
  18. 2023Chain of Hindsight Aligns Language Models with Feedbackcited by 148
  19. 2023Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Frameworkcited by 139
  20. 2023Retrieving Multimodal Information for Augmented Generation: A Surveycited by 85
  21. 2020KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Basecited by 78
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Citing papers per year: 2023: 1; 2024: 61; 2025: 44; 2026: 16.

122 papers.

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  2. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 320
  3. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
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  5. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  6. 2024Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similaritiescited by 159
  7. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 126
  8. 2025A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systemscited by 111
  9. 2024Why Tabular Foundation Models Should Be a Research Prioritycited by 106
  10. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  11. 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 86
  12. 2024Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluationcited by 80
38 more
  1. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Modelscited by 77
  2. 2024RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generationcited by 75
  3. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generationcited by 64
  4. 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 62
  5. 2024Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answeringcited by 60
  6. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 56
  7. 2025TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Usecited by 51
  8. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  9. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMscited by 48
  10. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 47
  11. 2024Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Modelscited by 44
  12. 2024Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoningcited by 44
  13. 2024GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Modelscited by 43
  14. 2024Goldfish: Vision-Language Understanding of Arbitrarily Long Videoscited by 40
  15. 2024Knowledge Boundary of Large Language Models: A Surveycited by 40
  16. 2025Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topicscited by 36
  17. 2024Improving Uncertainty Quantification in Large Language Models via Semantic Embeddingscited by 30
  18. 2024TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systemscited by 28
  19. 2023HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responsescited by 25
  20. 2024Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphscited by 24
  21. 2024Claim Verification in the Age of Large Language Models: A Surveycited by 24
  22. 2024Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Taskscited by 22
  23. 2024Estimating the Hallucination Rate of Generative AIcited by 20
  24. 2024KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Modelscited by 20
  25. 2024Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answeringcited by 19
  26. 2024SFR-RAG: Towards Contextually Faithful LLMscited by 19
  27. 2024Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Modelscited by 18
  28. 2024Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Artcited by 17
  29. 2024An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaborationcited by 16
  30. 2025Multi-Agent Tool-Integrated Policy Optimizationcited by 16
  31. 2024Logical Consistency of Large Language Models in Fact-checkingcited by 15
  32. 2024Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillationcited by 14
  33. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Controlcited by 13
  34. 2024RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answeringcited by 13
  35. 2025Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applicationscited by 13
  36. 2024Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphscited by 12
  37. 2025HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoningcited by 12
  38. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 11

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