EMCEE: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context
arXiv:2503.05846v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric training data leads to significant performance degradation in non-English languages. While existing multilingual prompting methods emphasize reformulating queries into English or enhancing reasoning capabilities, they often fail to incorporate the language- and culture-specific grounding that is essential for some queries. To address this limitation, we propose EMCEE (Extracting synthetic Multilingual
The proliferation of LLMs and their English-centric development highlights an immediate need for better multilingual capabilities as their adoption expands globally.
Improving multilingual LLM performance addresses a foundational limitation, critical for global equitable AI access and preventing a widening digital divide.
LLMs can now more effectively process and understand non-English languages, reducing reliance on English-centric input and enhancing their utility across diverse linguistic contexts.
- · Non-English speaking AI users
- · Multinational corporations
- · AI developers focused on global markets
- · Governments seeking localized AI solutions
- · Monolingual English content producers
- · Translation services (long-term disruption)
Enhanced multilingual LLMs will enable more accurate and contextually relevant AI applications in non-English speaking regions.
This could accelerate AI adoption and innovation in previously underserved language communities, fostering new local AI ecosystems.
Over time, persistent language barriers in AI might diminish, leading to a more globally integrated and less English-dominated AI landscape.
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Read at arXiv cs.AI