Dynamic Retrieval Parameter Tuning for Cross-Domain Generalization in RAG Systems

Abstract

Abstract: Current AI-driven Question-Answering (QA) systems face significant challenges in delivering accurate, domainspecific responses across diverse fields. While Large Language Models (LLMs) excel at text generation, they often face difficulties in producing precise answers without access to external knowledge sources. Retrieval-Augmented Generation (RAG) addresses this limitation by connecting LLMs to external information sources. However, RAG systems often struggle to maintain the relevance and reliability of the retrieved information. Finetuning rerankers on domain-specific data could improve the

Research Question

Can dynamic retrieval parameter tuning improve cross-domain generalization in RAG systems compared to static configurations when evaluated on diverse multi-document QA datasets?

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