Cross Lingual Generalization

Cross-lingual generalization in large language models (LLMs) focuses on enabling models trained primarily on one language (often English) to effectively perform tasks in other languages. Current research investigates methods to improve this ability, including techniques like instruction tuning, preference tuning, and meta-learning, often applied to multilingual models such as mBERT, XLM-R, and BLOOM. This research is crucial for broadening the accessibility and applicability of LLMs globally, addressing biases stemming from data imbalances and promoting fairness and inclusivity in natural language processing.