Awesome-LLM-KG

Awesome-LLM-KG

Introduction

Awesome-LLM-KG is a comprehensive project dedicated to the exploration and unification of Large Language Models (LLMs) and Knowledge Graphs (KGs). With innovations rapidly unfolding in artificial intelligence, this project assembles a collection of academic papers and resources that explore the integration of these two powerful tools. While LLMs have shown exceptional versatility and generalization across various applications, they often struggle with accurately capturing and retrieving factual information. Mimicking human-like reasoning, KGs serve as structured data models that store factual knowledge but also face challenges due to their complex and dynamic nature. Therefore, combining LLMs and KGs provides a promising approach, taking advantage of the strengths of both technologies.

News

The Awesome-LLM-KG project is actively evolving, continually contributing to the academic community. Some recent milestones include the release of papers that examine reasoning on knowledge graphs using large models, the analysis of temporal KG reasoning with LLMs, and discussing the systematic assessment of factual knowledge within LLMs. Highlights include work accepted by esteemed conferences like NeurIPS, ACL, EMNLP, and ICLR, showcasing the project's significance and impact in the AI community.

Overview

The project repository serves as a roadmap, highlighting three major frameworks that guide the unification of LLMs and KGs:

1. KG-enhanced LLMs: Elevating LLM capabilities with the enriching factual content found within KGs.
2. LLMs-augmented KGs: Enhancing KGs with the language generalization prowess of LLMs.
3. Synergized LLMs + KGs: A balanced and integrated approach to leverage both technologies effectively.

Each framework is supported by detailed diagrams that elucidate the involved methodologies and their diverse applications. This roadmap was designed to assist researchers and practitioners in delving deeper into this burgeoning field.

Table of Contents

The project repository is organized with a robust Table of Contents, making it easy for users to navigate through different sections, which include:

• Related surveys
• KG-enhanced LLMs: Pre-training, inference, and interpretability
• LLM-augmented KGs: Embedding, completion, generation, and question answering
• Synergized LLMs + KGs: Knowledge representation and reasoning
• Applications: Focused on recommendation systems and fault analysis

Detailed Sections

Related Surveys

The repository provides an extensive list of surveys related to both LLMs and KGs. These documents serve as a foundational resource for understanding the current state of the art and key developments in knowledge-enhanced language models and knowledge-intensive NLP tasks.

KG-enhanced LLMs

This section addresses ways to improve large language models by incorporating knowledge from graphs. Techniques focus on pre-training LLMs with enriched datasets from KGs, enhancing inference processes, and interpreting model outputs in a knowledgeable context.

LLM-augmented KGs

This portion of the framework focuses on transforming knowledge graphs using the language intuition and capabilities of LLMs. It includes approaches like KG embedding, completion, KG-to-text generation, and question answering, which all benefit from LLM integration.

Synergized LLMs + KGs

In this dual approach, efforts are made to develop systems where LLMs and KGs work in synergy, focusing on enhancing knowledge representation and building reasoning models that benefit from both structured and unstructured data.

Applications

The repository also explores real-world applications where combined LLM and KG systems can be applied. It includes case studies in recommendation systems—where personalization and factual accuracy are crucial—and fault analysis, which can benefit from a model's ability to both reason and access structured data efficiently.

Conclusion

The Awesome-LLM-KG project presents a wealth of knowledge and ongoing research at the intersection of large language models and knowledge graphs. By integrating these two technologies, the project aims to tackle existing limitations and push the boundaries of artificial intelligence in understanding and processing complex, multi-faceted data.