BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval Augmented Generation
Year: 2024

Large language models excel at creative generation but continue to struggle with the issues of hallucination and bias.While retrievalaugmented generation (RAG) provides a framework for grounding LLMs' responses in accurate and up-to-date information, it still raises the question of bias: which sources should be selected for inclusion in the context?And how should their importance be weighted?In this paper, we study the challenge of cross-lingual RAG and present a dataset to investigate the robus