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This paper evaluates the sensitivity of the Knowledge Graph-Augmented Retrieval Generation (KG-RAG) method to the incompleteness of knowledge graphs (KGs). KG-RAG, which improves large-scale language model (LLM) inference in tasks such as question answering (QA), relies on incomplete KGs in the real world, which may omit information necessary for answering questions. This study systematically manipulates the incompleteness of KGs by removing ternary tuples in various ways and analyzes the impact. This experimentally demonstrates the performance degradation of KG-RAG methods. Consequently, it highlights the need for more robust KG-RAG approaches in real-world settings.
Takeaways, Limitations
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Takeaways: This is the first study to systematically evaluate the impact of incomplete real-world knowledge graphs on the performance of KG-RAG. By exposing the vulnerability of KG-RAG, we suggest the need for more robust models. By clearly demonstrating the performance degradation of KG-RAG under incomplete KGs, we suggest future research directions.
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Limitations: Further research is needed to determine the generalizability of the proposed KG incompleteness manipulation method. It may have focused on a specific type of KG incompleteness, and a wider range of incompleteness types should be considered. The type of KG-RAG method used in the evaluation may be limited, and research that incorporates a broader methodology is needed.
