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CoCoA: Context-Conditional Cultural Alignment for Large Language Models

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Large Language Models (LLMs) often favor Western-associated entities across cultural contexts. Conventional debiasing methods aim for uniform neutrality, but cultural bias mitigation demands context-conditional behavior, preferring culturally appropriate entities when cultural cues are present and remaining neutral when they are absent. We propose CoCoA (Context-Conditional Cultural Alignment), a framework that learns this behavior through dual-context training on the same entity pairs under contexts with and without cultural cues. CoCoA combines a contrastive alignment objective with calibrat

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Evidence & attribution

First collected: 2026-09-21T07:31:56.984Z. This is not the publication date.