Introduces Trust-RAG Compass framework and TRC Bench benchmark to assess RAG trustworthiness across factuality, robustness, fairness, transparency, accountability, and privacy, with evaluations showing performance gaps between LLMs.
The good and the bad: Exploring privacy issues in retrieval-augmented generation (RAG)
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PRAG delivers end-to-end private RAG with 72-74% recall via non-interactive homomorphic approximations, interactive client assistance, and operation-error estimation to preserve ranking quality.
ADAM extracts data from LLM agent memory with up to 100% attack success rate by estimating data distribution and selecting queries via entropy guidance.
GuarantRAG improves RAG accuracy up to 12.1% and cuts hallucinations 16.3% by decoupling parametric reasoning from evidence integration via contrastive DPO and joint decoding.
citing papers explorer
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Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
Introduces Trust-RAG Compass framework and TRC Bench benchmark to assess RAG trustworthiness across factuality, robustness, fairness, transparency, accountability, and privacy, with evaluations showing performance gaps between LLMs.
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PRAG: End-to-End Privacy-Preserving Retrieval-Augmented Generation
PRAG delivers end-to-end private RAG with 72-74% recall via non-interactive homomorphic approximations, interactive client assistance, and operation-error estimation to preserve ranking quality.
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ADAM: A Systematic Data Extraction Attack on Agent Memory via Adaptive Querying
ADAM extracts data from LLM agent memory with up to 100% attack success rate by estimating data distribution and selecting queries via entropy guidance.
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Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
GuarantRAG improves RAG accuracy up to 12.1% and cuts hallucinations 16.3% by decoupling parametric reasoning from evidence integration via contrastive DPO and joint decoding.