The bottleneck is retrieval, not reading: diagnosing evidence-based generation with RAG for long context question answering
Read the original at arxiv.org→arXiv:2610.26951v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunked pipeline has...
Original headline: "The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering"
Coverage timeline
- Oct 21, 04:00 UTC arXiv cs.CL lead source The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering
- Oct 22, 04:00 UTC arXiv cs.AI A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
- Oct 22, 04:00 UTC arXiv cs.CL Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG