@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://preview.aclanthology.org/ingest-acl/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 $\texttt{CLaS-Bench}$, a lightweight parallel-question benchmark for evaluating language-forcing behavior in LLMs across 32 languages, enabling systematic evaluation of multilingual steering methods. We evaluate a broad array of steering techniques, including residual-stream DiffMean interventions, probe-derived directions, language-specific neurons, PCA/LDA vectors, Sparse Autoencoders, and prompting baselines. Steering performance is measured along two axes: language control and semantic relevance, combined into a single harmonic-mean steering score. We find that across languages simple residual-based DiffMean method consistently outperforms all other methods. Moreover, a layer-wise analysis reveals that language-specific structure emerges predominantly in later layers and steering directions cluster based on language family. $\texttt{CLaS-Bench}$ is the first standardized benchmark for multilingual steering, enabling both rigorous scientific analysis of language representations and practical evaluation of steering as a low-cost adaptation alternative."
}