DIA-HARM evaluates 16 harmful content detection models across 50 English dialects using 195K+ samples, revealing 1.4–3.6% F1 drops for fine-tuned models and up to 27% for zero-shot …
BLUFF is the largest multilingual fake news detection benchmark, spanning 79 languages with 202K+ samples. It introduces AXL-CoI for adversarial generation and mPURIFY for quality …
This IEEE article provides empirical measurements of LLM-generated texts in multilingual disinformation, moving beyond speculation to analyze the growing presence and …
Beemo introduces a novel benchmark featuring 6.5k expert-edited machine-generated texts across diverse domains from creative writing to summarization. Through comprehensive …
This research from Penn State and KiNiT, benchmarks the effectiveness of 10 authorship obfuscation (AO) techniques against 37 machine-generated text (MGT) detection methods across …
The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
This research from Penn State and KiNiT introduces MULTITuDE, a novel multilingual dataset for detecting machine-generated text. Comprised of over 74,000 authentic and …
This research project is a collaboration with Penn State and MIT Lincoln Lab. Our study demonstrates the dual capacity of LLMs for offensive misuse and defense detection against …