Written by Rahul Kumar, Head of AI Engineering at Neurons Lab. Rahul is a 3x business founder, 2x author, AI scientist, deep learning practitioner, and independent researcher.
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Integrating a Knowledge Graph (KG) with a retrieval-augmented generation (RAG) system creates a powerful hybrid known as G-RAG. This integration enhances information retrieval, data visualization, clustering, and segmentation while mitigating issues like hallucination in LLMs.
As research and development continue, the potential for G-RAG systems to revolutionize various industries becomes increasingly apparent.
By leveraging the strengths of both KGs and RAG systems, we can create more accurate, reliable, and efficient AI solutions. The future of G-RAG is bright, promising significant advancements in how we retrieve, generate, and utilize information.
The power of Knowledge Graphs
In the rapidly evolving landscape of artificial intelligence, enhancing the performance of RAG systems has become a significant focus for researchers and practitioners. One promising approach to achieving this goal is integrating Knowledge Graphs (KGs) with RAG systems, resulting in a more powerful and efficient hybrid: G-RAG. In this article, I will explore how KGs can enhance RAG systems.
Knowledge Graphs are structured representations of knowledge, where entities are nodes connected by relationships (edges). They excel at capturing and organizing information in a way that is both human-readable and machine-actionable.
By integrating KGs with RAG systems, we can significantly enhance their ability to understand, retrieve, and generate relevant and accurate information.
One of the primary benefits of using KGs in RAG systems is their ability to expand the domain of information retrieval. By increasing the depth and breadth of nodes in a KG, the system can extract information from a more extensive and interconnected set of data points.
This expanded domain allows for more comprehensive responses and richer contextual understanding. For instance, by adjusting the parameters of the KG, such as the number of nodes and the depth of relationships, the system can extract a wider array of information. This approach enables the RAG system to retrieve detailed and nuanced responses, going beyond the capabilities of traditional language models.
But why Knowledge Graphs?
Through a series of rigorous evaluations, we extensively tested the performance of the LLM compared to the KG+LLM model, yielding truly remarkable results. In these comprehensive assessments, participants interacted with the RAG application and provided feedback based on three distinct levels of performance:
- Success: Reflecting instances where the model excelled in delivering accurate results.
- Failed: Denoting scenarios where the model struggled to generate satisfactory outcomes.
- Partial: Indicating situations where the model’s performance was partially correct.
- Success Rate: The KG + LLM model has a 34.75% higher success rate than the Vanilla LLM model.
- Failure Rate: The failure rate increased by approximately 48.17% with the KG + LLM model.
- Partial Rate: The partial rate decreased by approximately 54.62% with the KG + LLM model.
Vanilla LLM:
Success Rate: 30.56%
Failed: 27.78%
Partial Success: 38.89%
KG + LLM:
Success Rate: 41.18%
Failed: 41.18%
Partial Success: 17.65%
The KG + LLM model not only boosts the success rate by an incredible 34.75% compared to the Vanilla LLM, but also shows a remarkable 54.62% decrease in partial successes, indicating more definitive outcomes.
While the failure rate has increased, this indicates the model’s decisive nature in handling complex tasks.
Enhancing information retrieval
Integrating KGs with RAG systems can also refine the process of information retrieval. By leveraging the structured nature of KGs, the system can provide more accurate and contextually relevant answers. For example, when querying how to contact a company, the KG can supply precise contact details such as phone numbers and addresses, which may not be easily retrievable through standard LLMs.
This ability to extract and present factual information is crucial for applications that require high precision and reliability and hence helps fight against the hallucination problem. [55.43]
KG 101: Understanding the basics
To appreciate how Knowledge Graphs (KGs) enhance Retrieval-Augmented Generation (RAG) systems, it’s essential to grasp the fundamental concepts of KGs, including triplet formation, graph neural networks (GNNs), and the roles of nodes and entities.
Triplets Formation
A Knowledge Graph represents knowledge as a set of triplets, each consisting of:
- Head (Subject): The main entity.
- Relation (Predicate): The type of relationship.
- Tail (Object): The related entity.
For example, “Neurons Lab (Head) is located in (Relation) Europe (Tail)” captures a specific fact.
Graph Neural Networks (GNNs)
Graph Neural Networks are specialized for graph-structured data, enhancing KGs by:
- Capturing Relationships: Modeling both direct and indirect connections.
- Propagating Information: Spreading information across the graph layers to learn rich representations.
- Generalizing: Adapting to various graph types for different tasks.
GNNs improve tasks like node classification and link prediction within KGs.
Nodes and Entities
In KGs:
- Nodes (Entities): Represent significant objects or concepts, like people, departments, or products.
- Edges (Relationships): Define connections between nodes, such as “works in” or “located at.”
Visualizing and analyzing data
Another significant advantage of integrating KGs with RAG systems is the improved capability for data visualization and analysis. Graph embeddings, which preserve the relationships and structure within a KG, enable sophisticated visualizations that can reveal patterns and insights not immediately apparent from raw data.
By plotting these sub-graphs or respective embeddings, we can see how different entities and their relationships are organized within the KG. This visualization helps in understanding the underlying structure and connections, making it easier to analyze and interpret the data. [57:20]
Controlling hallucination in LLMs
A well-known challenge in using language models is the issue of hallucination, where the model generates plausible but incorrect or nonsensical information. KGs can help mitigate this problem by providing a structured and factual basis for information retrieval and generation.
By setting parameters like temperature to zero, we can reduce the likelihood of hallucination but this is not enough since by default the nature of any LM is designed to predict the next token.
Where is in the context of KG, we are computing the most semantically similar relations and facts that exist in the KG and use that as the context for the LLM to limit its search spectrum which ensures that the generated responses are grounded in the factual data stored in the KG. [01:01:24]
Future directions: Fine-tuning and specialized models
The integration of KGs with RAG systems opens up exciting possibilities for future development. While effective prompting can enhance triplet generation (the creation of entity-relationship-entity structures), fine-tuning specialized models can further improve performance and quality.
Research in graph language models and graph representation learning is ongoing, with the potential to significantly advance the capabilities of G-RAG systems. By continuing to explore and develop these technologies, we can create more sophisticated and effective solutions for a wide range of applications.
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