Large language models (LLMs) often default to Modern Standard Arabic (MSA) when generating Arabic, even when prompted with dialectal Arabic. A natural explanation is that their internal representations are dominated by MSA. We test this hypothesis by adapting the language-dominance framework of Shani and Basirat (2025)...
Abdu Sallouh, Nicholas Popovic, Michael Färber· 0 citations
This work introduces the first large-scale benchmark for UQ calibration in long-form, reasoning-demanding scientific QA, evaluating four UQ methods on 685,000 responses across up to 20 LLMs and seven datasets, supported by an extensible open-source framework whose shared-generation design enables reproducible cross-met...
Philip Müller, Nicholas Popovic, Michael Färber et al.· 4 citations· ⚡1
Experiments on the OneStopEnglish corpus using multiple state-of-the-art LLMs show that incorporating retrieved examples consistently improves simplification quality over prompt-only generation and achieves competitive or superior performance compared with representative supervised (T5) and planning-based (PlanSimp) do...
Marina Litvak, Ariel Perstin, Ilan Shtilman et al.· 0 citations
This work study large language model (LLM)-based simplification of scientific texts and presents a human-in-the-loop workflow that transforms expert summaries into more accessible versions for non-specialists.
Kyuri Im, Michael Färber· arXiv.org· 0 citations
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