mlsanityData to 14 Oct 2026

Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models

arXiv 2310.16570 ↗First posted 25 Oct 2023cs.CLcited by 32

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Abstract (arXiv)

Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich in world knowledge. This fact has sparked the interest of the community in quantifying the amount of factual knowledge present in PLMs, as this explains their performance on downstream tasks, and potentially justifies their use as knowledge bases. In this work, we survey methods and datasets that are used to probe PLMs for factual knowledge. Our contributions are: (1) We propose a categorization scheme for factual probing methods that is based on how their inputs, outputs and the probed PLMs are adapted; (2) We provide an overview of the datasets used for factual probing; (3) We synthesize insights about knowledge retention and prompt optimization in PLMs, analyze obstacles to adopting PLMs as knowledge bases and outline directions for future work.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 89 of its 116 references that are in the map. First posted on arXiv 25 Oct 2023.

Builds on

89 of 116 references.

References · 89

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38 more
  1. 2019Language Models as Knowledge Bases?cited by 1,936
  2. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  3. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,392
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  11. 2021Editing Factual Knowledge in Language Modelscited by 553
  12. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  13. 2019What do you learn from context? Probing for sentence structure in contextualized word representationscited by 438
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  20. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 296
  21. 2021Factual Probing Is [MASK]: Learning vs. Learning to Recallcited by 291
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Citing papers per year: 2024: 13; 2025: 12; 2026: 7.

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  1. 2024Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Modelscited by 235
  2. 2024A Comprehensive Study of Knowledge Editing for Large Language Modelscited by 163
  3. 2024On Large Language Models' Hallucination with Regard to Known Factscited by 43
  4. 2024Large Scale Knowledge Washingcited by 16
  5. 2024Monotonic Representation of Numeric Properties in Language Modelscited by 15
  6. 2025Position: Editing Large Language Models Poses Serious Safety Riskscited by 15
  7. 2024Has this Fact been Edited? Detecting Knowledge Edits in Language Modelscited by 14
  8. 2024Understanding Knowledge Drift in LLMs through Misinformationcited by 10
  9. 2024How to Make LLMs Forget: On Reversing In-Context Knowledge Editscited by 9
  10. 2024Beyond Memorization: The Challenge of Random Memory Access in Language Modelscited by 8
  11. 2024How Well Do LLMs Identify Cultural Unity in Diversity?cited by 8
  12. 2025Do We Know What LLMs Don't Know? A Study of Consistency in Knowledge Probingcited by 8
20 more
  1. 2024The Queen of England is not England's Queen: On the Lack of Factual Coherency in PLMscited by 7
  2. 2025Tracing and Reversing Edits in LLMscited by 7
  3. 2026STRUCTSURVEY: Structured Agentic Retrieval for Automated Survey Paper Generationcited by 4
  4. 2025Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddingscited by 3
  5. 2025Comparing Knowledge Injection Methods for LLMs in a Low-Resource Regimecited by 2
  6. 2025Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptationcited by 2
  7. 2026One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Themcited by 2
  8. 2024Enhancing Fact Retrieval in PLMs through Truthfulnesscited by 1
  9. 2025Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editingcited by 1
  10. 2025TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generationcited by 1
  11. 2025Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspectivecited by 1
  12. 2025Injecting Falsehoods: Adversarial Man-in-the-Middle Attacks Undermining Factual Recall in LLMscited by 1
  13. 2026Persuasion Tokens for Editing Factual Knowledge in LLMscited by 1
  14. 2026Self-Aware Knowledge Probing: Evaluating Language Models' Relational Knowledge through Confidence Calibrationcited by 1
  15. 2026Probing for Knowledge Attribution in Large Language Modelscited by 1
  16. 2024Beyond Individual Facts: Investigating Categorical Knowledge Locality of Taxonomy and Meronomy Concepts in GPT Modelscited by 0
  17. 2025Latent Knowledge Scalpel: Precise and Massive Knowledge Editing for Large Language Modelscited by 0
  18. 2025FIBER: A Multilingual Evaluation Resource for Factual Inference Biascited by 0
  19. 2026Eliciting associations between clinical variables from LLMs via comparison questions across populationscited by 0
  20. 2026The Privacy-Hallucination Tradeoff in Differentially Private Language Modelscited by 0