Retrieval-Augmented Generation for Question Answering over a Research Paper: A Hybrid Dense-Keyword Retrieval Study on the Transformer Architecture Paper
Retrieval-Augmented Generation (RAG) combines information retrieval with language generation to improve the grounding of generated answers in a source document, addressing a central weakness of purely parametric large language models: the tendency to produce fluent but unsupported statements. This study designs, implem...