Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/assertion>. }
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- RetrievalAugmentedGeneration type Workflow assertion.
- arXiv.2402.06764 type Entity assertion.
- FewShotPrompting type Workflow assertion.
- GLaMLLMSummarization type Workflow assertion.
- GLaMNodeDescriptorsAdjacencySummarization type Workflow assertion.
- GLaMRelationalGrouping type Workflow assertion.
- GLaMTriples type Workflow assertion.
- GNNLLMJointModelCoupling type Workflow assertion.
- GNNLLMSoftPrompting type Workflow assertion.
- RetrievalAugmentedGeneration label "Retrieval Augmented Generation (RAG)" assertion.
- FewShotPrompting label "Few-Shot Prompting" assertion.
- GLaMLLMSummarization label "GLaM (LLM Summarization)" assertion.
- GLaMNodeDescriptorsAdjacencySummarization label "GLaM (Node Descriptors, Adjacency Lists, and Summarization Combination)" assertion.
- GLaMRelationalGrouping label "GLaM (Relational Grouping)" assertion.
- GLaMTriples label "GLaM (Triples)" assertion.
- GNNLLMJointModelCoupling label "GNN-LLM Joint Model Coupling" assertion.
- GNNLLMSoftPrompting label "GNN-LLM Soft Prompting" assertion.
- GLaMLLMSummarization comment "This method leverages the LLM's generative capabilities to rewrite or summarize the encoded graph statements into more coherent representations for fine-tuning. The goal is to enhance semantic alignment between the KG and the LLM's vocabulary, thereby improving the LLM's factual recall and multi-hop reasoning after training." assertion.
- GLaMNodeDescriptorsAdjacencySummarization comment "This method combines multiple encoding strategies, specifically using LLM's zero-shot capabilities to create text-based node descriptors from the k-hop context subgraph, utilizing adjacency lists, and performing summarization. This comprehensive approach aims to instill robust graph-based reasoning capabilities into the LLM via fine-tuning." assertion.
- GLaMRelationalGrouping comment "This GLaM variant encodes the neighborhood subgraph by including the entire adjacency list of the central node or partitioning neighbors based on relation types. This strategy is used to fine-tune the LLM to better understand graph structure for improved knowledge expression." assertion.
- GLaMTriples comment "This method is a specific implementation within the GLaM framework where the neighborhood subgraph is encoded into (source, relation, target) triples for fine-tuning. It aims to improve the LLM's factual reasoning by embedding graph knowledge directly into its parameters during the training phase." assertion.
- arXiv.2402.06764 describes GLaMLLMSummarization assertion.
- arXiv.2402.06764 describes GLaMNodeDescriptorsAdjacencySummarization assertion.
- arXiv.2402.06764 describes GLaMRelationalGrouping assertion.
- arXiv.2402.06764 describes GLaMTriples assertion.
- arXiv.2402.06764 discusses RetrievalAugmentedGeneration assertion.
- arXiv.2402.06764 discusses FewShotPrompting assertion.
- arXiv.2402.06764 discusses GNNLLMJointModelCoupling assertion.
- arXiv.2402.06764 discusses GNNLLMSoftPrompting assertion.
- GLaMLLMSummarization subject KGEnhancedLLMPretraining assertion.
- GLaMNodeDescriptorsAdjacencySummarization subject KGEnhancedLLMPretraining assertion.
- GLaMRelationalGrouping subject KGEnhancedLLMPretraining assertion.
- GLaMTriples subject KGEnhancedLLMPretraining assertion.
- arXiv.2402.06764 title "GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding" assertion.
- GLaMLLMSummarization hasTopCategory KGEnhancedLLM assertion.
- GLaMNodeDescriptorsAdjacencySummarization hasTopCategory KGEnhancedLLM assertion.
- GLaMRelationalGrouping hasTopCategory KGEnhancedLLM assertion.
- GLaMTriples hasTopCategory KGEnhancedLLM assertion.