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20172026
most citedAdapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters

8 citations · 16 across the 48 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

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…

cs.CL2026

Disentangling Mathematical Reasoning in LLMs: A Methodological Investigation of Internal Mechanisms

Tanja Baeumel, Josef van Genabith, Simon Ostermann

Large language models (LLMs) have demonstrated impressive capabilities, yet their internal mechanisms for handling reasoning-intensive tasks remain underexplored. To advance the un…

cs.CL2026

From Weights to Activations: Is Steering the Next Frontier of Adaptation?

Simon Ostermann, Daniil Gurgurov, Tanja Baeumel +4

Post-training adaptation of language models is commonly achieved through parameter updates or input-based methods such as fine-tuning, parameter-efficient adaptation, and prompting…

cs.CL2026

ReasonXL: Shifting LLM Reasoning Language Without Sacrificing Performance

Daniil Gurgurov, Tom Röhr, Sebastian von Rohrscheidt +3

Despite advances in multilingual capabilities, most large language models (LLMs) remain English-centric in their training and, crucially, in their production of reasoning traces. E…

cs.CL2026

CLaS-Bench: A Cross-Lingual Alignment and Steering Benchmark

Daniil Gurgurov, Yusser Al Ghussin, Tanja Baeumel +5

Understanding and controlling the behavior of large language models (LLMs) is an increasingly important topic in multilingual NLP. Beyond prompting or fine-tuning, , i.e.,~manipula…