GraphRAGs Reduce Hallucinations by 20% in LLM Apps

GraphRAGs, or Graph Retrieval-Augmented Generation systems, outperform traditional vector store-backed RAGs by reducing hallucinations in large language model (LLM) applications. While vector databases are effective in most scenarios, they require explicit text mentions for accurate context retrieval. GraphRAGs, however, excel when retrieval involves reasoning, decreasing the occurrence of hallucinations by about 20%. Despite this improvement, GraphRAGs do not eliminate hallucinations entirely. The article discusses the importance of measuring and evaluating these systems to understand their performance better. It also mentions alternative retrieval models like ColBERT and Multi-representation, which can enhance RAG applications.

Source: towardsdatascience.com

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