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cs.IR2025
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers
Navve Wasserman, Oliver Heinimann, Yuval Golbari +3
Rerankers play a critical role in multimodal Retrieval-Augmented Generation (RAG) by refining ranking of an initial set of retrieved documents. Rerankers are typically trained usin…
cs.IR2025
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark
Navve Wasserman, Roi Pony, Oshri Naparstek +4
Accurate multi-modal document retrieval is crucial for Retrieval-Augmented Generation (RAG), yet existing benchmarks do not fully capture real-world challenges with their current d…