arXiv:2601.04885v2 Announce Type: replace Abstract: As Large Language Models (LLMs) serve a global audience, alignment must transition from enforcing universal consensus to respecting cultural pluralism. We demonstrate that dense models, when forced to fit conflicting value distributions, suffer from \textbf{Mean Collapse}, converging to a generic average that fails to represent diverse groups. We attribute this to \textbf{Cultural Sparsity}, where gradient interference prevents dense parameters from spanning distinct cultural modes. To resolve this, we propose \textbf{\textsc{CuMA}} (\textbf{
As LLMs become global products, the need to adapt them to diverse cultural contexts beyond Western defaults is critical for market acceptance and utility.
This research addresses a fundamental limitation in current LLM alignment, paving the way for models that are culturally nuanced rather than homogeneously generic, which is vital for broader adoption and combating 'mean collapse' of values.
The proposed 'Mixture of Adapters' approach offers a technical path to integrate sparse, distinct cultural values into LLMs without diluting or distorting them, moving beyond a one-size-fits-all alignment strategy.
- · LLM developers
- · Global consumers of AI
- · Localized content creators
- · Governments seeking culturally aligned AI
- · LLMs with 'universal consensus' alignment
- · Homogenized online content providers
- · Cultures underrepresented in training data
LLMs will be capable of reflecting and interacting within specific cultural value systems more effectively.
This could accelerate the adoption of LLMs in diverse linguistic and cultural markets, fostering new applications and services.
The ability to customize cultural values could lead to 'sovereign AI' models tailored to national or specific community values, potentially increasing digital fragmentation but also relevance.
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Read at arXiv cs.CL