Search Results for author:
Found 6 papers, 4 papers with code
LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph
Next, through a Transformer-based Knowledge Adapter, it finely extracts and integrates factual and structural information from the KG, then maps this information to the LLM's token embedding space, creating an LLM-friendly prompt to be used by the LLM for the final reasoning.
PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
Typical RAG approaches split the text database into chunks, organizing them in a flat structure for efficient searches.
LightRAG: Simple and Fast Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs.
Intent-aware Recommendation via Disentangled Graph Contrastive Learning
The other is different behaviors have different intent distributions, so how to establish their relations for a more explainable recommender system.
GraphEdit: Large Language Models for Graph Structure Learning
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures.
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks.
