从提示工程到因果检索，系统梳理大模型增强方法
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
- 按推理时结构化上下文程度统一分类增广策略
- 构建文献筛选与证据合成框架，提升结论可信度
- 提供部署决策工具，指导可信赖的检索增强应用
大语言模型虽在参数中存储海量世界知识，但仍受限于静态知识、有限上下文窗口和弱结构化因果推理。本文沿单一维度——推理时提供的结构化上下文程度——系统梳理增强策略，涵盖上下文学习与提示工程、检索增强生成（RAG）、GraphRAG 和因果检索增强生成（CausalRAG）。超越概念对比，本文提出透明的文献筛选协议、主张审计框架与结构化跨论文证据合成，区分高置信度发现与新兴结果。最后给出面向部署的决策框架与可信赖检索增强NLP的研究优先级。
原文摘要 · Abstract (English)
Large language models (LLMs) encode vast world knowledge in their parameters, yet they remain fundamentally limited by static knowledge, finite context windows, and weakly structured causal reasoning. This survey provides a unified account of augmentation strategies along a single axis: the degree of structured context supplied at inference time. We cover in-context learning and prompt engineering, Retrieval-Augmented Generation (RAG), GraphRAG, and CausalRAG. Beyond conceptual comparison, we provide a transparent literature-screening protocol, a claim-audit framework, and a structured cross-paper evidence synthesis that distinguishes higher-confidence findings from emerging results. The paper concludes with a deployment-oriented decision framework and concrete research priorities for trustworthy retrieval-augmented NLP.
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