DGurgurov/CLaS-Bench

Tasks

• text-generation
• translation

Citation text from the card

@inproceedings{gurgurov-etal-2026-clas,
    title = "{CL}a{S}-Bench: A Cross-Lingual Alignment and Steering Benchmark",
    author = "Gurgurov, Daniil  and
      Al Ghussin, Yusser  and
      Baeumel, Tanja  and
      Chou, Cheng-Ting  and
      Schramowski, Patrick  and
      Mosbach, Marius  and
      van Genabith, Josef  and
      Ostermann, Simon",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1086/",
    doi = "10.18653/v1/2026.findings-acl.1086",
    pages = "21591--21628",
    ISBN = "979-8-89176-395-1",
    abstract = "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 interpretable technique for adapting models to a target language. Yet, no dedicated benchmarks or evaluation protocols exist to quantify the effectiveness of steering techniques. We introduce $CLaS-Bench$, a lightweight parallel-question benchmark for evaluating language-forcing behavior in LLMs across 32

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