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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FaithfulRAG introduces fact-level conflict modeling to improve context faithfulness in retrieval-augmented generation, with potential extensions to multimodal RAG and trustworthy AI.
Vikash Singh, Debargha Ganguly, Aman Goel, et al.
Introduces Verdict-Preserving-Unfaithfulness (VPU) in autoformalization and proposes Generative Verification (GenV), a reference-free continuous equivalence score achieving 0.961 AUROC.
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RAGAS is a framework for reference-free evaluation of RAG systems, assessing retrieval, faithfulness, and response quality.