Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG

• URL: http://arxiv.org/abs/2410.02825v2
• Date: Fri, 11 Oct 2024 18:37:37 GMT
• Title: Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG
• Authors: Chenhao Fang, Derek Larson, Shitong Zhu, Sophie Zeng, Wendy Summer, Yanqing Peng, Yuriy Hulovatyy, Rajeev Rao, Gabriel Forgues, Arya Pudota, Alex Goncalves, Hervé Robert,
• Abstract summary: 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.
• Score: 2.7972592976232833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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