# Language Models as Fact Checkers?

arXiv: https://arxiv.org/abs/2006.04102 · HTML: https://mlsanity.com/paper/2006.04102/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 30 of 51)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Calibrating Factual Knowledge in Pretrained Language Models](https://mlsanity.com/paper/2210.03329/) (2022)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://mlsanity.com/paper/2203.08383/) (2022)
- [Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources](https://mlsanity.com/paper/2112.00061/) (2021)
- [Towards Few-Shot Fact-Checking via Perplexity](https://mlsanity.com/paper/2103.09535/) (2021)
- [GooAQ: Open Question Answering with Diverse Answer Types](https://mlsanity.com/paper/2104.08727/) (2021)
- [Synthetic Disinformation Attacks on Automated Fact Verification Systems](https://mlsanity.com/paper/2202.09381/) (2022)
- [CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets](https://mlsanity.com/paper/2204.12164/) (2022)
- [NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias](https://mlsanity.com/paper/2204.04902/) (2022)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification](https://mlsanity.com/paper/2407.02352/) (2024)
- [Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge](https://mlsanity.com/paper/2403.05189/) (2024)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models](https://mlsanity.com/paper/2305.01645/) (2023)
- [ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs](https://mlsanity.com/paper/2403.09724/) (2024)
- [More Victories, Less Cooperation: Assessing Cicero's Diplomacy Play](https://mlsanity.com/paper/2406.04643/) (2024)
- [Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets](https://mlsanity.com/paper/2209.07834/) (2022)
- [Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems](https://mlsanity.com/paper/2211.12143/) (2022)
- [Using Persuasive Writing Strategies to Explain and Detect Health Misinformation](https://mlsanity.com/paper/2211.05985/) (2022)
- [Unsupervised Improvement of Factual Knowledge in Language Models](https://mlsanity.com/paper/2304.01597/) (2023)
- [Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses](https://mlsanity.com/paper/2402.17097/) (2024)
- [Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media](https://mlsanity.com/paper/2204.10080/) (2022)
- [Unsupervised Pretraining for Fact Verification by Language Model Distillation](https://mlsanity.com/paper/2309.16540/) (2023)

## Abstract (arXiv)

Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers. In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our fine-tuned LM compares favorably with standard baselines. Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.