Research: Evaluating Hallucination in Retrieval-Augmented Language Models for Domain-Sp...

Research question: How do retrieval-augmented generation models impact hallucination rates in domain-grounded question answering, and what mechanisms can mitigate these errors?

Retrieval-augmented generation (RAG) models combine large language models with external knowledge sources to improve accuracy and grounding in question answering tasks. These architectures have shown promise in reducing hallucinations—instances where models generate factually incorrect or unsupported answers—by leveraging domain-specific databases.

Despite advances, hallucination remains a critical problem in RAG systems, especially when answering questions in highly specialized domains such as medicine, law, or science. Existing literature lacks systematic analysis of hallucination patterns and mitigation strategies specific to domain-grounded retrieval augmentation.

This project will quantitatively and qualitatively analyze hallucination rates across several RAG architectures (e.g., Fusion-in-Decoder, Retro) using benchmark datasets in targeted domains. It will design controlled experiments to identify contributing factors, evaluate mitigation techniques (e.g., answer verification, citation enforcement), and propose best practices for deployment in sensitive applications.

Understanding how retrieval mechanisms interact with generative models to affect factuality in domain-specific QA is vital for trustworthy AI. This research will inform future model design, enhance safety, and support adoption in scientific, medical, and legal contexts where reliability is paramount.