# BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages

arXiv: https://arxiv.org/abs/2603.00634 · HTML: https://mlsanity.com/paper/2603.00634/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2308.08747/) (2023)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [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)
- [Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation](https://mlsanity.com/paper/2310.15515/) (2023)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [Jailbreaking and Mitigation of Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2410.15236/) (2024)
- [Multilingual Previously Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2305.07991/) (2023)
- [Disinformation Capabilities of Large Language Models](https://mlsanity.com/paper/2311.08838/) (2023)
- [No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection](https://mlsanity.com/paper/2010.06906/) (2020)
- [How Vocabulary Sharing Facilitates Multilingualism in LLaMA?](https://mlsanity.com/paper/2311.09071/) (2023)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [Machine Translation Models are Zero-Shot Detectors of Translation Direction](https://mlsanity.com/paper/2401.06769/) (2024)
- [Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media](https://mlsanity.com/paper/2304.03797/) (2023)

## Built on by (most cited first, 2 of 2)

- [Fair Fact-Checking: Closing the Cross-Lingual Gap in LLM Factual Judgement with RoSh](https://mlsanity.com/paper/2609.34678/) (2026)
- [Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh](https://mlsanity.com/paper/2610.15870/) (2026)

## Abstract (arXiv)

Multilingual falsehoods threaten information integrity worldwide, yet detection benchmarks remain confined to English or a few high-resource languages, leaving low-resource linguistic communities without robust defense tools. We introduce BLUFF, a comprehensive benchmark for detecting false and synthetic content, spanning 79 languages with over 202K samples, combining human-written fact-checked content (122K+ samples across 57 languages) and LLM-generated content (79K+ samples across 71 languages). BLUFF uniquely covers both high-resource "big-head" (20) and low-resource "long-tail" (59) languages, addressing critical gaps in multilingual research on detecting false and synthetic content. Our dataset features four content types (human-written, LLM-generated, LLM-translated, and hybrid human-LLM text), bidirectional translation (EnglishleftrightarrowX), 39 textual modification techniques (36 manipulation tactics for fake news, 3 AI-editing strategies for real news), and varying edit intensities generated using 19 diverse LLMs. We present AXL-CoI (Adversarial Cross-Lingual Agentic Chainof-Interactions), a novel multi-agentic framework for controlled fake/real news generation, paired with mPURIFY, a quality filtering pipeline ensuring dataset integrity. Experiments reveal state-of-theart detectors suffer up to 25.3% F1 degradation on low-resource versus high-resource languages. BLUFF provides the research community with a multilingual benchmark, extensive linguistic-oriented benchmark evaluation, comprehensive documentation, and opensource tools to advance equitable falsehood detection. Dataset and code are available at: https://jsl5710.github.io/BLUFF/