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

arXiv: https://arxiv.org/abs/2310.16570 · HTML: https://mlsanity.com/paper/2310.16570/

## 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 (most cited first, 30 of 89)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://mlsanity.com/paper/2104.06599/) (2021)
- [Measuring and Improving Consistency in Pretrained Language Models](https://mlsanity.com/paper/2102.01017/) (2021)
- [Entity-Based Knowledge Conflicts in Question Answering](https://mlsanity.com/paper/2109.05052/) (2021)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)

## Built on by (most cited first, 30 of 32)

- [Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models](https://mlsanity.com/paper/2401.06102/) (2024)
- [A Comprehensive Study of Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2401.01286/) (2024)
- [On Large Language Models' Hallucination with Regard to Known Facts](https://mlsanity.com/paper/2403.20009/) (2024)
- [Large Scale Knowledge Washing](https://mlsanity.com/paper/2405.16720/) (2024)
- [Monotonic Representation of Numeric Properties in Language Models](https://mlsanity.com/paper/2403.10381/) (2024)
- [Position: Editing Large Language Models Poses Serious Safety Risks](https://mlsanity.com/paper/2502.02958/) (2025)
- [Has this Fact been Edited? Detecting Knowledge Edits in Language Models](https://mlsanity.com/paper/2405.02765/) (2024)
- [Understanding Knowledge Drift in LLMs through Misinformation](https://mlsanity.com/paper/2409.07085/) (2024)
- [How to Make LLMs Forget: On Reversing In-Context Knowledge Edits](https://mlsanity.com/paper/2410.12586/) (2024)
- [Beyond Memorization: The Challenge of Random Memory Access in Language Models](https://mlsanity.com/paper/2403.07805/) (2024)
- [How Well Do LLMs Identify Cultural Unity in Diversity?](https://mlsanity.com/paper/2408.05102/) (2024)
- [Do We Know What LLMs Don't Know? A Study of Consistency in Knowledge Probing](https://mlsanity.com/paper/2505.21701/) (2025)
- [The Queen of England is not England's Queen: On the Lack of Factual Coherency in PLMs](https://mlsanity.com/paper/2402.01453/) (2024)
- [Tracing and Reversing Edits in LLMs](https://mlsanity.com/paper/2505.20819/) (2025)
- [STRUCTSURVEY: Structured Agentic Retrieval for Automated Survey Paper Generation](https://mlsanity.com/paper/2607.01243/) (2026)
- [Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings](https://mlsanity.com/paper/2508.06030/) (2025)
- [Comparing Knowledge Injection Methods for LLMs in a Low-Resource Regime](https://mlsanity.com/paper/2508.06178/) (2025)
- [Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation](https://mlsanity.com/paper/2511.05852/) (2025)
- [One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them](https://mlsanity.com/paper/2605.28839/) (2026)
- [Enhancing Fact Retrieval in PLMs through Truthfulness](https://mlsanity.com/paper/2410.13562/) (2024)
- [Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing](https://mlsanity.com/paper/2503.11895/) (2025)
- [TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generation](https://mlsanity.com/paper/2504.18269/) (2025)
- [Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspective](https://mlsanity.com/paper/2508.16969/) (2025)
- [Injecting Falsehoods: Adversarial Man-in-the-Middle Attacks Undermining Factual Recall in LLMs](https://mlsanity.com/paper/2511.05919/) (2025)
- [Persuasion Tokens for Editing Factual Knowledge in LLMs](https://mlsanity.com/paper/2601.16781/) (2026)
- [Self-Aware Knowledge Probing: Evaluating Language Models' Relational Knowledge through Confidence Calibration](https://mlsanity.com/paper/2601.18901/) (2026)
- [Probing for Knowledge Attribution in Large Language Models](https://mlsanity.com/paper/2602.22787/) (2026)
- [Beyond Individual Facts: Investigating Categorical Knowledge Locality of Taxonomy and Meronomy Concepts in GPT Models](https://mlsanity.com/paper/2406.15940/) (2024)
- [Latent Knowledge Scalpel: Precise and Massive Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2508.03741/) (2025)
- [FIBER: A Multilingual Evaluation Resource for Factual Inference Bias](https://mlsanity.com/paper/2512.11110/) (2025)

## 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.