AI RESEARCH
Language Steering for Multilingual In-Context Learning
arXiv CS.CL
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ArXi:2602.02326v2 Announce Type: replace If large language models operate in a universal semantic space, then switching between languages should require only a simple activation offset. To test this, we take multilingual in-context learning as a, where few-shot nstrations are provided in English but the test query is in a target language.