通过质量评估和重新检索，自动检测并纠正低质量检索结果的 RAG 系统
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模式： 在标准 RAG 基础上增加显式检索质量评估,并依据置信度评分执行纠正操作
原因： 通过质量评估与自适应纠正策略减少幻觉并提升准确性
关键洞察： 三层纠正策略:高置信度结果精炼,中置信度补充,低置信度重新检索
通过这些精选资源加深理解
Corrective Retrieval Augmented Generation (Yan et al., 2024)
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection (Asai et al., 2023)
Active Retrieval Augmented Generation (Jiang et al., 2023)
Lost in the Middle: How Language Models Use Long Contexts (Liu et al., 2023)
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Chain-of-Verification Reduces Hallucination in Large Language Models (Dhuliawala et al., 2023)
Factuality Enhanced Language Models for Open-Ended Text Generation (Lee et al., 2022)