PAS encodes locations via relative anchors and bins to deliver roughly 370-400m adversarial error in spatial RAG while retaining over half the baseline retrieval performance and keeping generation quality robust.
Rageval: Scenario specific rag evaluation dataset generation framework
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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VisRAG achieves 20-40% better end-to-end performance than text-based RAG by directly embedding and retrieving document images with VLMs.
SeedRG generates novel, leakage-free RAG benchmark examples from seed data by mapping reasoning structures and swapping entities while applying consistency and leakage checks.
citing papers explorer
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Privacy Without Losing Place: A Paradigm for Private Retrieval in Spatial RAGs
PAS encodes locations via relative anchors and bins to deliver roughly 370-400m adversarial error in spatial RAG while retaining over half the baseline retrieval performance and keeping generation quality robust.
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VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
VisRAG achieves 20-40% better end-to-end performance than text-based RAG by directly embedding and retrieving document images with VLMs.
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Generating Leakage-Free Benchmarks for Robust RAG Evaluation
SeedRG generates novel, leakage-free RAG benchmark examples from seed data by mapping reasoning structures and swapping entities while applying consistency and leakage checks.