  Large language models (LLMs) typically utilize the top-k contexts from a
retriever in retrieval-augmented generation (RAG). In this work, we propose a
novel instruction fine-tuning framework RankRAG, which instruction-tunes a
single LLM for the dual purpose of context ranking and answer generation in
RAG. In particular, the instruction-tuned LLMs work surprisingly well by adding
a small fraction of ranking data into the training blend, and outperform
existing expert ranking models, including the same LLM exclusively fine-tuned
on a large amount of ranking data. For generation, we compare our model with
many strong baselines, including GPT-4-0613, GPT-4-turbo-2024-0409, and
ChatQA-1.5, an open-sourced model with the state-of-the-art performance on RAG
benchmarks. Specifically, our Llama3-RankRAG significantly outperforms
Llama3-ChatQA-1.5 and GPT-4 models on nine knowledge-intensive benchmarks. In
addition, it also performs comparably to GPT-4 on five RAG benchmarks in the
biomedical domain without instruction fine-tuning on biomedical data,
demonstrating its superb capability for generalization to new domains.
