NVIDIA Introduces RankRAG: A Novel RAG Framework that Instruction-Tunes a Single LLM for the Dual Purposes of Top-k Context Ranking and Answer Generation in RAG
What improvements does RankRAG show on challenging datasets?
RankRAG demonstrates substantial improvements on challenging datasets, such as long-tailed QA (PopQA) and multi-hop QA (2WikimQA), with over 10% improvement compared to ChatQA-1.5. These results suggest that RankRAG's context ranking capability is particularly effective in scenarios where top retrieved documents are less relevant to the answer, enhancing performance in complex OpenQA tasks2.
How does RankRAG's training process differ from traditional methods?
RankRAG's training process differs from traditional methods by incorporating a two-stage instruction tuning approach. It first involves supervised fine-tuning on diverse instruction-following datasets. The second stage unifies ranking and generation tasks, incorporating context-rich QA, retrieval-augmented QA, context ranking, and retrieval-augmented ranking data. All tasks are standardized into a (question, context, answer) format, facilitating knowledge transfer.
What unique approach does RankRAG introduce for context ranking?
RankRAG introduces a novel instruction fine-tuning framework that trains a single language model to perform both context ranking and answer generation in retrieval-augmented generation (RAG). It incorporates a small fraction of ranking data into the training blend, enabling the model to outperform dedicated ranking models and excel in generating relevant responses.