A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper investigates whether long-context language models can replace traditional tools like retrieval systems and databases for knowledge-intensive tasks.
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CEMRAG integrates interpretable visual concept extraction with multimodal RAG to improve both interpretability and factual accuracy in radiology report generation.
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HierFinRAG uses hierarchical multimodal RAG with Symbolic–Neural Fusion to close 60–80% of the human–AI performance gap on financial document understanding.
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This paper proposes QIMG-7 and source-aware resolution to improve multimodal RAG reliability by resolving conflicts before fusion.
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This paper proposes a reliable multimodal RAG method for medical vision-language models to improve factual accuracy.
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This paper explores optimal configurations for multimodal RAG systems, showing they can outperform single-modality RAG in industrial applications.
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Introduces a unified benchmark for multimodal RAG evaluation using real-world PDF documents and a data synthesizing pipeline.
Junxiao Xue, Quan Deng, Fei Yu, et al.
Enhanced multimodal RAG-LLM framework for accurate visual question answering by integrating retrieval-augmented generation with multimodal data.
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Formalizes and addresses parametric-retrieved and visual-textual knowledge inconsistencies in multimodal RAG via cross-source reconciliation.
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M4-RAG is an evaluation framework for multilingual, multicultural, and multimodal RAG that focuses on end-to-end task performance and systematic failure analysis.
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MMed-RAG is a versatile multimodal RAG system designed for medical vision-language models to generate more factual responses.
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This paper proposes enhancing graph-based retrieval-augmented generation (RAG) with robust retrieval techniques to improve factual accuracy.