Academic

Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis

arXiv:2603.05698v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) was introduced to enhance the capabilities of Large Language Models (LLMs) beyond their encoded prior knowledge. This is achieved by providing LLMs with an external source of knowledge, which helps reduce factual hallucinations and enables access to new information not available during pretraining. However, inconsistent retrieved information can negatively affect LLM responses. The Retrieval-Augmented Generation Benchmark (RGB) was introduced to evaluate the robustness of RAG systems under such conditions. In this work, we use the RGB corpus to evaluate LLMs in four scenarios: noise robustness, information integration, negative rejection, and counterfactual robustness. We perform a comparative analysis between the RGB RAG baseline and GraphRAG, a knowledge graph based retrieval system. We test three GraphRAG customizations to improve robustness. Results show improvements over the RGB baseline and prov

H
Hazem Amamou, St\'ephane Gagnon, Alan Davoust, Anderson R. Avila · · 1 min read · 3 views
#cs.CL

arXiv:2603.05698v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) was introduced to enhance the capabilities of Large Language Models (LLMs) beyond their encoded prior knowledge. This is achieved by providing LLMs with an external source of knowledge, which helps reduce factual hallucinations and enables access to new information not available during pretraining. However, inconsistent retrieved information can negatively affect LLM responses. The Retrieval-Augmented Generation Benchmark (RGB) was introduced to evaluate the robustness of RAG systems under such conditions. In this work, we use the RGB corpus to evaluate LLMs in four scenarios: noise robustness, information integration, negative rejection, and counterfactual robustness. We perform a comparative analysis between the RGB RAG baseline and GraphRAG, a knowledge graph based retrieval system. We test three GraphRAG customizations to improve robustness. Results show improvements over the RGB baseline and provide insights for designing more reliable RAG systems for real world scenarios.

Executive Summary

This study explores the application of knowledge graphs in Retrieval-Augmented Generation (RAG) systems to enhance their robustness and reliability. By leveraging the Retrieval-Augmented Generation Benchmark (RGB) corpus, the authors conduct a comparative analysis between the RGB RAG baseline and GraphRAG, a knowledge graph-based retrieval system. The results demonstrate improvements in noise robustness, information integration, negative rejection, and counterfactual robustness. The study's findings have significant implications for the design of more reliable RAG systems in real-world scenarios, particularly in applications where accuracy and trustworthiness are paramount. The authors' customization of GraphRAG to address known limitations highlights the potential for iterative improvement in RAG systems.

Key Points

  • ▸ The study evaluates the robustness of RAG systems using the RGB corpus in four scenarios.
  • ▸ GraphRAG, a knowledge graph-based retrieval system, outperforms the RGB RAG baseline in noise robustness, information integration, negative rejection, and counterfactual robustness.
  • ▸ Customizations to GraphRAG improve its robustness, providing insights for designing more reliable RAG systems.

Merits

Comprehensive Evaluation Framework

The study employs a robust evaluation framework, the RGB corpus, to assess the performance of RAG systems in various scenarios.

Knowledge Graph-Based Approach

The authors' use of knowledge graphs in GraphRAG demonstrates the potential for leveraging structured knowledge in improving RAG systems' robustness.

Customization and Improvement

The study's customization of GraphRAG to address known limitations showcases the iterative improvement potential in RAG systems.

Demerits

Limited Generalizability

The study's findings may not be directly applicable to all RAG systems, particularly those with different architectures or training datasets.

Lack of Human Evaluation

The study relies solely on automated evaluation metrics, which may not capture the full range of human evaluators' preferences and expectations.

Expert Commentary

The study makes a valuable contribution to the ongoing research in RAG systems, particularly in the context of knowledge graph-based approaches. The authors' customization of GraphRAG to address known limitations highlights the potential for iterative improvement in RAG systems. However, the study's limitations, such as the reliance on automated evaluation metrics, should be addressed in future research. The study's findings have significant implications for the design of more reliable RAG systems in real-world scenarios, particularly in applications where accuracy and trustworthiness are paramount.

Recommendations

  • ✓ Future research should investigate the generalizability of the study's findings to other RAG systems and architectures.
  • ✓ The use of human evaluation metrics in addition to automated metrics would provide a more comprehensive understanding of RAG systems' performance.

Sources

Related Articles