A Retrieval-augmented Generation Framework with Retriever and Generator Modules for Enhancing Factual Consistency

Large Language Models (LLMs) are powerful but often produce factually incorrect content (hallucinations), limiting their reliability in knowledge-intensive tasks. Retrieval-augmented generation (RAG) is a promising approach to mitigate this issue by grounding LLM outputs in external knowledge sources. The paper proposes an RAG framework integrating a retriever and generator module to improve factual consistency. The retriever first identifies relevant documents from large-scale datasets, and the generator then produces context-aware responses based on the retrieved evidence. This study evaluates the approach on open-domain question answering benchmarks, including Natural Questions, Microsoft Machine Reading Comprehension Dataset (MS MARCO), and Covid-19 Open Research Dataset (CORD-19). The RAG-augmented model significantly reduces the hallucination rate from 68% to 10% and increases the Knowledge F1 score from 17.7 to 26.0, outperforming a baseline LLM without retrieval. These results demonstrate that augmenting LLMs with retrieval substantially enhances their factual accuracy and reliability. This improvement is significant for time-sensitive and domain-specific applications, such as healthcare and legal contexts, where up-to-date and accurate information is critical.