3 个 · RAG 检索增强 · 收藏榜
收录 AIGC 领域 RAG 的优秀论文。我们在论文 "Retrieval-Augmented Generation for AI-Generated Content: A Survey" 中提出了 RAG 基础、增强与应用分类法。Collecting awesome papers of RAG for AIGC. We propose a taxonomy of RAG foundations, enhancements, and applications in paper "Retrieval-Augmented Generation for AI-Generated Content: A Survey".
按性价比排序的循证生活指南：长寿防病、急救、省钱理财、法律红线、失业与工伤、医保社保、恋爱婚育、怀孕育儿、创业与做平台合规、出国与技能。每条写明成本、收益、证据等级和原始出处，只引期刊论文与官方文件。
LLM 的知识截止会使文献综述过时或不完整；RAG 与时序感知检索可提升时效性，但无法完全解决引用、来源选择与时间推理问题。LLM knowledge cutoffs can make literature reviews outdated or incomplete, while RAG and time-aware retrieval can improve freshness but do not fully solve citation, source-selection, and temporal reasoning problems.