NeocorRAG uses Evidence Chains to achieve SOTA retrieval quality in RAG on HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ for 3B and 70B models while using under 20% of the tokens of comparable methods.
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Anonymization placement in RAG—at the dataset or at the generated answer—creates observable differences in privacy protection versus response utility.
The survey unifies LLM augmentation techniques along the single axis of structured context supplied at inference time and supplies a literature screening protocol plus deployment decision framework.
citing papers explorer
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NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains
NeocorRAG uses Evidence Chains to achieve SOTA retrieval quality in RAG on HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ for 3B and 70B models while using under 20% of the tokens of comparable methods.
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A Case Study on the Impact of Anonymization Along the RAG Pipeline
Anonymization placement in RAG—at the dataset or at the generated answer—creates observable differences in privacy protection versus response utility.
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Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
The survey unifies LLM augmentation techniques along the single axis of structured context supplied at inference time and supplies a literature screening protocol plus deployment decision framework.