Simon Ostermann

Post-training adaptation of large language models is commonly achieved through parameter updates or input based methods such as fine-tuning, parameter-efficient adaptation, and prompting. In parallel, a growing body of work modifies internal activations at inference time to influence model behavi...

Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models (LLMs) beyond the training domain, while supervised fine-tuning (SFT) frequently leads to general capabilities forgetting. However, the mechanisms underlying this contrast remain uncl...

Understanding and controlling the behavior of large language models (LLMs) is an increasingly important topic in multilingual NLP. Beyond prompting or fine-tuning, \textit{language steering} , i.e., manipulating internal representations during inference, has emerged as a more efficient and interp...

Evaluating factual correctness in procedural video captions is challenging because captions must reflect both the abstract procedural roles (e.g., actions, ingredients, tools, locations) and their visual execution. Existing evaluation metrics, which rely on lexical overlap or holistic semantic si...