🤖 AI Summary
Large language models are constrained by static knowledge, limited context windows, and weak causal reasoning capabilities, hindering their effective use of external information. This work systematically reviews techniques that enhance model performance during inference through structured contextual augmentation, including in-context learning, prompt engineering, retrieval-augmented generation (RAG), graph-augmented RAG, and causal RAG. We propose a unified analytical framework that integrates literature synthesis, cross-study evidence aggregation, and claim auditing to distinguish high-confidence findings from emerging results. Building on this framework, we develop a deployment-oriented decision guide and release a prioritized research agenda for trustworthy retrieval-augmented NLP, offering systematic support for future research and practical applications.
📝 Abstract
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.