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Related Experiment Video

Updated: Mar 21, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Knowledge Graph and Large Language Model Co-learning via Structure-oriented Retrieval Augmented Generation.

Carl Yang¹, Ran Xu¹, Linhao Luo¹

¹Department of Computer Science, Emory University, Atlanta, GA 30322, USA.

Bulletin of the Technical Committee on Data Engineering

|March 20, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) struggle with factual accuracy. This study explores co-learning between LLMs and knowledge graphs (KGs) to improve reasoning and data utilization for trustworthy AI.

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Published on: June 13, 2025

Area of Science:

Artificial Intelligence
Data Science
Knowledge Representation

Background:

Large language models (LLMs) show great potential but lack factual reliability and reasoning capabilities.
Real-world data is complex, multimodal, and challenging to integrate using traditional methods.
Current knowledge graph (KG) construction requires significant human expertise and labor.

Purpose of the Study:

To address the limitations of LLMs in factual knowledge and reasoning.
To explore methods for efficient and expert-driven knowledge graph construction.
To advance trustworthy artificial intelligence through the integration of LLMs and KGs.

Main Methods:

Discussing LLM-aided KG construction and KG-guided LLM enhancement.
Introducing a structure-oriented retrieval augmented generation (SRAG) paradigm.
Exploring knowledge-aware multi-agent federation.

Main Results:

Demonstrating progress in the co-learning of KGs and LLMs.
Highlighting the potential of SRAG for improved reasoning and data grounding.
Showcasing advancements in utilizing complex, multimodal data.

Conclusions:

The co-learning of KGs and LLMs offers a promising path towards enhancing AI reliability and reasoning.
The SRAG paradigm facilitates better integration of structured knowledge with generative models.
This research expedites the development of next-generation trustworthy AI systems.