2 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation
Andrei C. Coman, Ionut-Teodor Sorodoc, Leonardo F. R. Ribeiro +3
Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfuln…
GaRAGe: A Benchmark with Grounding Annotations for RAG Evaluation
Ionut-Teodor Sorodoc, Leonardo F. R. Ribeiro, Rexhina Blloshmi +2
We present GaRAGe, a large RAG benchmark with human-curated long-form answers and annotations of each grounding passage, allowing a fine-grained evaluation of whether LLMs can iden…
XRAG: Cross-lingual Retrieval-Augmented Generation
Wei Liu, Sony Trenous, Leonardo F. R. Ribeiro +2
We propose XRAG, a novel benchmark designed to evaluate the generation abilities of LLMs in cross-lingual Retrieval-Augmented Generation (RAG) settings where the user language does…