Retriever-Generator Co-Training for Hallucination Reduction in Knowledge-Intensive Tasks

Abstract

Abstract: Large Language Models (LLMs) excel in language comprehension and generation but are prone to hallucinations, producing factually incorrect or unsupported outputs. Retrieval Augmented Generation (RAG) systems address this issue by grounding LLM responses with external knowledge. This study evaluates the relationship between retriever effectiveness and hallucination reduction in LLMs using three retrieval approaches: sparse retrieval based on BM25 keyword search, dense retrieval using semantic search with Sentence Transformers, and a proposed hybrid retrieval module. The hybrid module

Research Question

How does retriever-generator co-training influence factuality scores and hallucination rates in knowledge-intensive downstream applications?

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Provenance