arXiv (All)
open access
Conformal Factuality Control for Multi-Hop Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) can ground large language models in external evidence, but retrieved context does not guarantee that generated claims are factually supported. This problem is especially relevant in multi-hop RAG, where retrieval and reasoning proceed through multiple dependent s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation#1104536
- When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation#659189
- When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation#657053
- UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities#821696
- Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs#973310
Record · ID 1142823
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive