RET-LLM [Modarressi et al., 2023] constructs a per-
extracting
sonalized
future use.
relation triples
SUGRE[Kang et al., 2023]
subgraphs
retrieved from the knowledge graph using Graph Neural
Networks (GNN) to prevent
the model from generating
contextually irrelevant replies. SUGRE[Kang et al., 2023]
employs a graph encoding method that reflects the graph
structure into PTMs’ representation space and utilizes a
multi-modal contrastive learning objective between graph-
text modes to ensure consistency between retrieved facts
and generated text. KnowledgeGPT[Wang et al., 2023c]
generates search queries for Knowledge Bases (KB) in code
format and includes predefined KB operation functions.
Apart from retrieval, KnowledgeGPT also offers the ca-
pability to store knowledge in a personalized knowledge
base to meet individual user needs. These structured data
sources provide RAG with richer knowledge and context,
contributing to improved model performance.
LLM Generated Content RAG
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey