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Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG
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This paper presents new methods that have the potential to improve privacy process efficiency with LLM and RAG. To reduce hallucination, we continually pre-train the base LLM model with a privacy-specific knowledge base and then augment it with a semantic RAG layer. Our evaluations demonstrate that this approach enhances the model performance (as much as doubled metrics compared to out-of-box LLM) in handling privacy-related queries, by grounding responses with factual information which reduces inaccuracies.
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Compliance Brain Assistant: Conversational Agentic AI for Assisting Compliance Tasks in Enterprise Environments
An enterprise compliance chatbot that chooses between a fast RAG path and a multi-step agent path reports higher keyword and judge pass rates than a plain LLM on internal benchmarks.
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