Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation

Understand

RALMs (Retrieval-Augmented Language Models) broaden their knowledge scope by incorporating external textual resources.

• However, the multilingual nature of global knowledge necessitates RALMs to handle diverse languages, a topic that has received limited research focus.
• In this work, we propose \textit{Futurepedia}, a carefully crafted benchmark containing parallel texts across eight representative languages.
• We evaluate six multilingual RALMs using our benchmark to explore the challenges of multilingual RALMs.