Dimensions of Factual Reliability in LLMs: Multilinguality and the Short–Long Form Gap

As Large Language Models (LLMs) become globally deployed for information-seeking tasks,
ensuring their factual reliability across diverse contexts has become paramount. Yet most
research on LLM hallucinations remains narrowly focused—either English-centric or limited to
controlled tasks like summarization and translation. This talk presents a comprehensive
investigation into factual alignment across two critical dimensions: languages and task formats.
First, we will talk about a large-scale study evaluating hallucination across 30 languages in
open-domain question answering, revealing surprising patterns in how factual accuracy varies
across linguistic and scaling contexts. Second, I will explore the factual alignment gap between
short and long-form responses, demonstrating how the same model can exhibit vastly different
reliability depending on response format. Together, these works provide a unified perspective on
LLM factuality "in the wild," offering diagnostic tools and insights for practitioners deploying
LLMs internationally and researchers working to build more trustworthy AI systems.
Speaker(s): , Saad
Unska 3, Zagreb, Grad Zagreb, Croatia, 10000