AI RESEARCH
Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization
arXiv CS.AI
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ArXi:2605.18772v1 Announce Type: cross Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge. Recent agentic RAG systems extend this paradigm with critical agents to evaluate model responses and iteratively refine outputs.