# FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking

arXiv: https://arxiv.org/abs/2404.19482 · HTML: https://mlsanity.com/paper/2404.19482/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [Generating Literal and Implied Subquestions to Fact-check Complex Claims](https://mlsanity.com/paper/2205.06938/) (2022)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [BRENDA: Browser Extension for Fake News Detection](https://mlsanity.com/paper/2005.13270/) (2020)

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

- [The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval](https://mlsanity.com/paper/2503.14828/) (2025)
- [LiveFC: A System for Live Fact-Checking of Audio Streams](https://mlsanity.com/paper/2408.07448/) (2024)
- [Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation](https://mlsanity.com/paper/2506.07211/) (2025)
- [The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking](https://mlsanity.com/paper/2602.09516/) (2026)
- [QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications](https://mlsanity.com/paper/2407.21441/) (2024)
- [DTELS: Towards Dynamic Granularity of Timeline Summarization](https://mlsanity.com/paper/2411.09297/) (2024)
- [Annotation Tool and Dataset for Fact-Checking Podcasts](https://mlsanity.com/paper/2502.01402/) (2025)
- [MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations](https://mlsanity.com/paper/2602.05692/) (2026)
- [Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification](https://mlsanity.com/paper/2509.13888/) (2025)
- [ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videos](https://mlsanity.com/paper/2509.20467/) (2025)
- [One LLM to Train Them All: Multi-Task Learning Framework for Fact-Checking](https://mlsanity.com/paper/2601.11293/) (2026)
- [Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs](https://mlsanity.com/paper/2606.08605/) (2026)

## Abstract (arXiv)

We introduce 'FactCheck Editor', an advanced text editor designed to automate fact-checking and correct factual inaccuracies. Given the widespread issue of misinformation, often a result of unintentional mistakes by content creators, our tool aims to address this challenge. It supports over 90 languages and utilizes transformer models to assist humans in the labor-intensive process of fact verification. This demonstration showcases a complete workflow that detects text claims in need of verification, generates relevant search engine queries, and retrieves appropriate documents from the web. It employs Natural Language Inference (NLI) to predict the veracity of claims and uses LLMs to summarize the evidence and suggest textual revisions to correct any errors in the text. Additionally, the effectiveness of models used in claim detection and veracity assessment is evaluated across multiple languages.