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KG-RAG: Bridging the Gap Between Knowledge and Creativity
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Ensuring factual accuracy while maintaining the creative capabilities of Large Language Model Agents (LMAs) poses significant challenges in the development of intelligent agent systems. LMAs face prevalent issues such as information hallucinations, catastrophic forgetting, and limitations in processing long contexts when dealing with knowledge-intensive tasks. This paper introduces a KG-RAG (Knowledge Graph-Retrieval Augmented Generation) pipeline, a novel framework designed to enhance the knowledge capabilities of LMAs by integrating structured Knowledge Graphs (KGs) with the functionalities of LLMs, thereby significantly reducing the reliance on the latent knowledge of LLMs. The KG-RAG pipeline constructs a KG from unstructured text and then performs information retrieval over the newly created graph to perform KGQA (Knowledge Graph Question Answering). The retrieval methodology leverages a novel algorithm called Chain of Explorations (CoE) which benefits from LLMs reasoning to explore nodes and relationships within the KG sequentially. Preliminary experiments on the ComplexWebQuestions dataset demonstrate notable improvements in the reduction of hallucinated content and suggest a promising path toward developing intelligent systems adept at handling knowledge-intensive tasks.
Forward citations
Cited by 5 Pith papers
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SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection
SlimRAG shows that an entity-aware inverted index without graphs can match or beat graph-based RAG retrieval on HotpotQA while using far fewer index tokens.
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CLARIFY: A Specialist-Generalist Framework for Accurate and Lightweight Dermatological Visual Question Answering
A specialist classifier feeding a pruned VLM with knowledge-graph grounding reports 82.1% diagnostic accuracy on a 39-image dermatology test set, about 18 percentage points above a fine-tuned VLM baseline.
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Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
A survey of LLM hallucination research that formalizes hallucination types and argues, via incompleteness and undecidability arguments, that hallucinations cannot be fully eliminated.
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