The article discusses the limitations of Naive RAG models in answering comparison queries and introduces Multi-Hop RAG as a solution. Multi-Hop RAG allows large language models to answer complex questions by retrieving and analyzing multiple pieces of evidence. It contrasts single-hop queries with multi-hop queries, showcasing the difference in complexity and reasoning required. Examples of multi-hop queries include inference queries, comparison queries, temporal queries, and null queries, each requiring different levels of reasoning and analysis. Multi-Hop RAG is seen as a significant advancement in question answering, enabling deeper insights by reasoning across multiple sources.
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Source link: https://medium.com/@ganeshkannappan/beyond-naive-rag-multi-hop-retrieval-augmented-generation-ba7e1d8b61ad?source=rss——llm-5
in AI Medium
Enhancing retrieval-augmented generation for advanced natural language processing #NLP
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