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[This article belongs to Volume - 58, Issue - 2]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-913
Title :
Reducing Hallucinations in Large Language Models through an Optimized Retrieval-Augmented Generation Pipeline
Rajbharath R, , Chandiran S, , Praveen Kumar D, , Raviraghav S, , Balaji R, , Jeganathan I, , Mohamad Siddiq S,
Keywords : Reducing Hallucinations, Large Language Models, Optimized Retrieval, reducing, hallucinations
Rajbharath R, , Chandiran S, , Praveen Kumar D, , Raviraghav S, , Balaji R, , Jeganathan I, , Mohamad Siddiq S,
Abstract : While developing Generative and Training Language Models, there is a problem called "hallucinations", which refers to generative language models producing responses that seem reasonable given a context but are actually factually incorrect. In this paper, we present a new Optimised Retrieval Augmented Generation Pipeline that seeks to eliminate the hallucination problem, by dynamically grounding responses to external, retrievable knowledge. We have developed a system that leverages the following:
Keywords : Reducing Hallucinations, Large Language Models, Optimized Retrieval, reducing, hallucinations