EMCEE: Enhancing LLMs' Multilingual Capability via Synthetic Context
Researchers propose EMCEE (Extracting synthetic Multilingual Context and merging), a framework to improve LLMs' performance in non-English languages. Current multilingual prompting methods often lack language- and culture-specific grounding. EMCEE extracts synthetic context from the LLM itself to uncover latent, language-specific knowledge, then merges it with reasoning outputs. The approach addresses the performance degradation of LLMs due to English-centric training data. The paper is available on arXiv (2503.05846).
Key facts
- EMCEE stands for Extracting synthetic Multilingual Context and merging.
- It addresses LLMs' performance degradation in non-English languages.
- The framework extracts synthetic context from the LLM itself.
- It merges contextual insight with reasoning-oriented outputs.
- Current multilingual prompting methods lack language- and culture-specific grounding.
- The paper is on arXiv with ID 2503.05846.
- LLMs rely heavily on English-centric training data.
- EMCEE is a simple yet effective framework.
Entities
Institutions
- arXiv