BLUFF: Benchmarking in Low-resoUrce Languages for detecting Falsehoods and Fake news

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 (Benchmarking in Low-resoUrce Languages for detecting Falsehoods and Fake news), 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 (English↔X), 39 textual modification techniques, and varying edit intensities generated using 19 diverse LLMs. We present AXL-CoI (Adversarial Cross-Lingual Agentic Chain-of-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-the-art detectors suffer up to 25.3% F1 degradation on low-resource versus high-resource languages.

Under Review 2026 — Datasets and Benchmarks Track