@inproceedings{al-ghussin-etal-2026-dfki,
    title = "{DFKI}-{MLT} at {S}em{E}val-2026 {TASK} 7: Steering Multilingual Models Towards Cultural Knowledge",
    author = "Al Ghussin, Yusser  and
      Gurgurov, Daniil  and
      Hamidullah, Yasser  and
      Van Genabith, Josef  and
      Espa{\~n}a-Bonet, Cristina  and
      Ostermann, Simon",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.322/",
    pages = "2548--2563",
    ISBN = "979-8-89176-414-9",
    abstract = "Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their cultural knowledge remains uneven across regions and languages. We present the DFKI-MLT system for SemEval-2026 Task 7 on cultural awareness, where we apply activation steering to multilingual LLMs using language vectors extracted from parallel FLORES data. Our method performs inference-time adaptation by adding language-specific steering vectors to the residual stream at a selected transformer layer, without any parameter updates. We participated in both the short-answer (SAQ) and multiple-choice (MCQ) tracks; however, only our MCQ submission received an official score. In the official MCQ track, we achieved 86.96{\%} accuracy, ranking 7th out of 17 teams. To better understand system behavior, we conduct post-hoc analyses on the shared-task MCQ and SAQ settings. These analyses show that activation steering yields modest and heterogeneous improvements on cultural reasoning: gains are strongly layer-sensitive, vary substantially across language{--}region pairs (some configurations even degrade performance), and interact with prompt formulation (generic vs. culturally conditioned prompts). Our findings suggest that prompt design and activation steering should be jointly optimized for culturally aware multilingual inference. We release our code and experimental configurations at https://github.com/Yusser96/SemEval-2026-Track7."
}