Robert Moro | Jason S. Lucas

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

Sun, 01 Feb 2026 00:00:00 +0000

BLUFF is the largest multilingual fake news detection benchmark to date, spanning 79 languages (20 high-resource “big-head” + 59 low-resource “long-tail”) with over 202,000 samples. The benchmark combines human-written fact-checked content from 130 IFCN-certified organizations with LLM-generated content from 19 diverse models.

Key contributions include:

AXL-CoI (Adversarial Cross-Lingual Agentic Chain-of-Interactions): A multi-agentic framework using 10 fake chains and 8 real chains for controlled multilingual content generation
mPURIFY: A 4-stage quality filtering pipeline with 32 features across 5 dimensions, ensuring dataset integrity through asymmetric evaluation thresholds
Bidirectional translation: English↔X coverage across 70+ languages with 4 prompt variants
Comprehensive evaluation: State-of-the-art detectors suffer up to 25.3% Macro-F1 degradation on low-resource versus high-resource languages

Resources:

Beyond speculation: Measuring the Growing Presence of LLM-generated texts in Multilingual Disinformation

Thu, 01 Jan 2026 00:00:00 +0000

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This work provides the first comprehensive empirical analysis of LLM-generated content in multilingual disinformation campaigns, offering crucial insights for developing cross-linguistic detection strategies.

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Status: This article has been accepted for publication in IEEE (2025). Full publication details including DOI and page numbers will be updated upon final publication.

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Authorship Obfuscation in Multilingual Machine-Generated Text Detection

Fri, 05 Jan 2024 00:00:00 +0000

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MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark

Tue, 05 Dec 2023 00:00:00 +0000

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