# WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipedia

arXiv: https://arxiv.org/abs/2109.00835 · HTML: https://mlsanity.com/paper/2109.00835/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2021.

## Built on by (most cited first, 12 of 12)

- [FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering](https://mlsanity.com/paper/2305.04329/) (2023)
- [WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia](https://mlsanity.com/paper/2111.08543/) (2021)
- [Pipeline and Dataset Generation for Automated Fact-checking in Almost Any Language](https://mlsanity.com/paper/2312.10171/) (2023)
- [MegaWika 2: A More Comprehensive Multilingual Collection of Articles and their Sources](https://mlsanity.com/paper/2508.03828/) (2025)
- [Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications](https://mlsanity.com/paper/2311.15716/) (2023)
- [Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval](https://mlsanity.com/paper/2512.00804/) (2025)
- [NCL-UoR at SemEval-2025 Task 3: Detecting Multilingual Hallucination and Related Observable Overgeneration Text Spans with Modified RefChecker and Modified SeflCheckGPT](https://mlsanity.com/paper/2503.01921/) (2025)
- [Characterizing Knowledge Manipulation in a Russian Wikipedia Fork](https://mlsanity.com/paper/2504.10663/) (2025)
- [Language-Agnostic Modeling of Source Reliability on Wikipedia](https://mlsanity.com/paper/2410.18803/) (2024)
- [Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine](https://mlsanity.com/paper/2505.24028/) (2025)
- [Toward Agentic RAG for Ukrainian](https://mlsanity.com/paper/2604.14896/) (2026)
- [An End-to-End Ukrainian RAG for Local Deployment. Optimized Hybrid Search and Lightweight Generation](https://mlsanity.com/paper/2604.22095/) (2026)

## Abstract (arXiv)

With the growth of fake news and disinformation, the NLP community has been working to assist humans in fact-checking. However, most academic research has focused on model accuracy without paying attention to resource efficiency, which is crucial in real-life scenarios. In this work, we review the State-of-the-Art datasets and solutions for Automatic Fact-checking and test their applicability in production environments. We discover overfitting issues in those models, and we propose a data filtering method that improves the model's performance and generalization. Then, we design an unsupervised fine-tuning of the Masked Language models to improve its accuracy working with Wikipedia. We also propose a novel query enhancing method to improve evidence discovery using the Wikipedia Search API. Finally, we present a new fact-checking system, the textitWikiCheck API that automatically performs a facts validation process based on the Wikipedia knowledge base. It is comparable to SOTA solutions in terms of accuracy and can be used on low-memory CPU instances.