Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs

Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain disproportionately biased toward high-resource languages. This leads to cross-lingual factual inconsistency, where they shift their empirical answer distributions based solely on the prompt language. We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages (German, Sp

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ethics.ai (21 July 2026), “Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs,” evidence record 12331, https://ethics.ai/record/12331 (originally published by arXiv).

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