collaborators

27 papers

cs.CL2026

Grounding or Guessing? Visual Signals for Detecting Hallucinations in Sign Language Translation

Yasser Hamidullah, Koel Dutta Chowdhury, Yusser Al Ghussin +4

Hallucination, where models generate fluent text unsupported by visual evidence, remains a major flaw in vision-language models and is particularly critical in sign language transl…

cs.CL2026

The Latin Substrate: How Language Models Represent and Mediate Script Choice

Daniil Gurgurov, Alan Saji, Katharina Trinley +2

Many languages are written in multiple scripts, requiring large language models (LLMs) to generate equivalent linguistic content in distinct orthographic forms. While prior work su…

cs.CL2026

DFKI-MLT at SemEval-2026 TASK 7: Steering Multilingual Models Towards Cultural Knowledge

Yusser Al Ghussin, Daniil Gurgurov, Yasser Hamidullah +3

Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their cultural knowledge remains uneven across regions and languages. We pre…

cs.CL2026

Multilingual Steering by Design: Multilingual Sparse Autoencoders and Principled Layer Selection

Yusser Al Ghussin, Daniil Gurgurov, Tanja Baeumel +3

Sparse autoencoders (SAEs) enable feature-level mechanistic interpretability and activation steering in large language models (LLMs), but SAE-based language control remains unrelia…

cs.AI2026

DualFact+: A Multimodal Fact Verification Framework for Procedural Video Understanding

Cennet Oguz, Yasser Hamidullah, Josef van Genabith +1

We introduce DualFact, a dual-layer, multimodal factuality evaluation framework for procedural video captioning. DualFact separates factual correctness into conceptual facts, captu…

cs.CL2026

Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models

Dan Shi, Zhuowen Han, Simon Ostermann +3

Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models (LLMs) beyond the training domain, while supervised fine-tuning (S…