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RAGs Do Not Reduce Hallucinations in LLMs
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- AbnAsia.org
- @steven_n_t
"The article discusses the limitations of Retrieval-Augmented Generation (RAG) models in reducing hallucinations in large language models (LLMs). RAGs are a sophisticated intersection of information retrieval and generative machine learning techniques that enhance text generation by leveraging a vast repository of information. However, despite their advanced architecture, RAGs struggle to eliminate hallucinations, which are misleading or factually incorrect generated content.
The root causes of this limitation can be traced back to the intricate mathematical formulations and assumptions embedded within the RAG framework. The article emphasizes that contextual relevance (RAGs) improve domain specificity, not hallucination. The only place hallucinations can be addressed is within the LLM. Retrieval is based on semantic similarity and maximizing log likelihoods and is not a mathematical framework for formal verification of facts.
The article also explains how RAGs are used, including document retrieval, context integration, and text generation. Adding contextual relevance through RAG does not reduce hallucinations in LLMs. Instead, it increases semantic relevance scores, which is not the same as hallucination of the system. The article suggests that hallucinations can only be fixed within the LLM itself, not through RAGs.
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