超越参数：大型语言模型中上下文丰富技术的综述：从上下文提示到因果检索增强生成
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
AI总结：
本文综述了大型语言模型中通过增加结构化上下文来提升性能的技术，涵盖上下文学习、提示工程、检索增强生成等方法，并提出透明的文献筛选协议和可信检索增强NLP的研究优先级。
AI中文摘要：
大型语言模型（LLMs）在参数中编码了大量世界知识，但它们仍然受限于静态知识、有限的上下文窗口和弱结构化的因果推理。本文提供了一种统一的账户，沿着单一轴线探讨增强策略：推理时提供的结构化上下文的程度。我们涵盖了上下文学习和提示工程、检索增强生成（RAG）、图RAG和因果RAG。除了概念比较外，我们还提供了一个透明的文献筛选协议、一个声明审计框架和一个结构化的跨论文证据综合，以区分高可信度发现和新兴结果。本文最后提出了面向部署的决策框架和可信检索增强NLP的具体研究优先级。
英文摘要：
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.
