# Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking

arXiv: https://arxiv.org/abs/2502.15419 · HTML: https://mlsanity.com/paper/2502.15419/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 28 of 28)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://mlsanity.com/paper/2406.08464/) (2024)
- [AlignScore: Evaluating Factual Consistency with a Unified Alignment Function](https://mlsanity.com/paper/2305.16739/) (2023)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [Fine-tuning Language Models for Factuality](https://mlsanity.com/paper/2311.08401/) (2023)
- [Combining Fact Extraction and Verification with Neural Semantic Matching Networks](https://mlsanity.com/paper/1811.07039/) (2018)
- [Towards Controllable Biases in Language Generation](https://mlsanity.com/paper/2005.00268/) (2020)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [Generating Scientific Claims for Zero-Shot Scientific Fact Checking](https://mlsanity.com/paper/2203.12990/) (2022)
- [DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows](https://mlsanity.com/paper/2402.10379/) (2024)
- [AmbiFC: Fact-Checking Ambiguous Claims with Evidence](https://mlsanity.com/paper/2104.00640/) (2021)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking](https://mlsanity.com/paper/2309.00240/) (2023)
- [Stance Prediction and Claim Verification: An Arabic Perspective](https://mlsanity.com/paper/2005.10410/) (2020)
- [Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore](https://mlsanity.com/paper/2402.18045/) (2024)
- [Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs](https://mlsanity.com/paper/2409.19759/) (2024)
- [ViWikiFC: Fact-Checking for Vietnamese Wikipedia-Based Textual Knowledge Source](https://mlsanity.com/paper/2405.07615/) (2024)

## Built on by (most cited first, 8 of 8)

- [A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages](https://mlsanity.com/paper/2506.12158/) (2025)
- [Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification](https://mlsanity.com/paper/2601.16278/) (2026)
- [RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets](https://mlsanity.com/paper/2510.06143/) (2025)
- [Kakugo: Distillation of Low-Resource Languages into Small Language Models](https://mlsanity.com/paper/2601.14051/) (2026)
- [HiFACTMix: A Code-Mixed Benchmark and Graph-Aware Model for EvidenceBased Political Claim Verification in Hinglish](https://mlsanity.com/paper/2508.10001/) (2025)
- [FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding](https://mlsanity.com/paper/2602.03417/) (2026)
- [Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation](https://mlsanity.com/paper/2606.18389/) (2026)
- [ARAFA: An LLM-Generated Arabic Fact-Checking Dataset](https://mlsanity.com/paper/2609.25833/) (2026)

## Abstract (arXiv)

Robust automatic fact-checking systems have the potential to combat online misinformation at scale. However, most existing research primarily focuses on English. In this paper, we introduce MultiSynFact, the first large-scale multilingual fact-checking dataset containing 2.2M claim-source pairs designed to support Spanish, German, English, and other low-resource languages. Our dataset generation pipeline leverages Large Language Models (LLMs), integrating external knowledge from Wikipedia and incorporating rigorous claim validation steps to ensure data quality. We evaluate the effectiveness of MultiSynFact across multiple models and experimental settings. Additionally, we open-source a user-friendly framework to facilitate further research in multilingual fact-checking and dataset generation.