Cross Lingual Question Answering

Cross-lingual question answering (CLQA) focuses on enabling question-answering systems to understand and respond to questions posed in one language while using information from another, bridging the language barrier in information access. Current research emphasizes developing robust multilingual models, often leveraging large language models (LLMs) and incorporating techniques like hybrid dense-sparse retrieval and self-knowledge distillation to improve performance, particularly for low-resource languages. This field is crucial for expanding access to information globally and fostering more equitable development of AI technologies, with applications ranging from e-commerce to legal information retrieval.