Cross-Lingual Consensus: Aligning Multilingual Cultural Knowledge via Multilingual Self-Consistency
arXiv:2605.22137v1 Announce Type: new Abstract: Although Large Language Models (LLMs) demonstrate strong capabilities across various tasks, they exhibit significant performance discrepancies across languages. While prompting LLMs in English typically yields the highest general performance, it often induces a Western-centric bias, hindering the model's ability to accurately reflect diverse cultural knowledge. We hypothesize that LLMs already possess rich cultural knowledge embedded within local-language representations, but fail to retrieve it when prompted in English. To bridge this cross-ling
This research addresses the ongoing challenge of cultural bias in large language models, a key issue as AI deployment expands globally and into diverse cultural contexts.
A strategic reader should care because overcoming Western-centric bias in LLMs is critical for equitable global AI adoption, market access in non-Western economies, and the development of truly universal AI agents.
This research suggests a method to unlock richer cultural knowledge within existing LLMs by prompting them in local languages, potentially reducing bias without needing entirely new models.
- · Non-Western language users
- · Multinational corporations
- · AI agents developers
- · Cultural preservation initiatives
- · Monolingual AI developers
- · Western-centric content platforms
Improved performance and cultural relevance of LLMs in diverse linguistic and cultural contexts.
Increased demand for local language data and expertise to fine-tune and prompt LLMs effectively.
Enhanced sovereign AI capabilities for nations developing models tailored to their specific linguistic and cultural nuances.
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Read at arXiv cs.CL