Publikation
CLaS-Bench: A Cross-Lingual Alignment and Steering Benchmark
Daniil Gurgurov; Yusser Al Ghussin; Tanja Bäumel; Cheng-Ting Chou; Patrick Schramowski; Marius Mosbach; Josef van Genabith; Simon Ostermann
In: Maria Liakata; Viviane P. Moreira; Jiajun Zhang; David Jurgens (Hrsg.). Findings of the Association for Computational Linguistics: ACL 2026. Annual Meeting of the Association for Computational Linguistics (ACL-2026), San Diego, California, United States, Pages 21591-21628, ISBN 979-8-89176-395-1, Association for Computational Linguistics, 2026.
Zusammenfassung
Understanding and controlling the behavior of large language models (LLMs) is an increasingly important topic in multilingual NLP. Beyond prompting or fine-tuning, $textitlanguage steering$, 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 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. $CLaS-Bench$ 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.