arXiv:2606. 13550v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) depends critically on the quality and granularity of retrieved evidence.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence.
arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.
arXiv:2607. 22597v1 Announce Type: new Abstract: Multi-hop question answering requires systems to retrieve evidence from multiple documents and connect scattered facts into a coherent reasoning process.
arXiv:2606. 29706v1 Announce Type: cross Abstract: Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language.
arXiv:2606. 28337v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are often evaluated using final answer accuracy, even though their failures can originate from preprocessing, retrieval, context packing, or generation.
arXiv:2504. 20734v5 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries.