CONVERSATIONAL AI

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

September 30, 2024

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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AUTHORS

Written by

Chenhao Fang

Derek Larson

Shitong Zhu

Sophie Zeng

Wendy Summer

Yanqing Peng

Yuriy Hulovatyy

Rajeev Rao

Gabriel Forgues

Arya Pudota

Alex Goncalves

Hervé Robert

Publisher

3rd ACM International Conference on Information and Knowledge Management (CIKM 2024)

Research Topics

Conversational AI

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