RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs (AI summary)

Key Points

1. Large language models (LLMs) traditionally utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG). The proposed novel framework, RankRAG, instruction-tunes a single LLM for the dual purpose of context ranking and answer generation in RAG. RankRAG significantly outperforms existing expert ranking models, including the same LLM exclusively fine-tuned on a large amount of ranking data, by adding a small fraction of ranking data into the training blend.

2. Retrieval-augmented generation (RAG) is a widely used technique for customizing LLMs to handle long-tail knowledge, provide up-to-date information, and adapt to specific domains and tasks without modifying the model weights. However, the current RAG pipeline has limitations related to the reading capacity of LLMs, challenges in ensuring high recall of relevant contents, and limitations of expert ranking models' generalization capability compared to LLMs.

3. RankRAG aims to design an RAG instruction tuning pipeline that uses a single language model to achieve both high-recall context extraction and high-quality content generation. The proposed framework, RankRAG, enhances LLM’s RAG capability through simultaneously instructing the LLM on context ranking and answer generation. The framework is readily applicable to diverse knowledge-intensive NLP tasks, and the training blend includes context-rich QA, retrieval-augmented QA, and ranking datasets to enhance the LLM’s ability to filter out irrelevant contexts during both the retrieval and generation phases of RAG.

4. RankRAG demonstrates a strong ability to extract answers from relevant context for a given question, a capability viewed as the "dual capability" of determining whether a chunk of context is relevant to the question and is useful for generating the answer. These capabilities mutually enhance each other. The design of RankRAG training, which involves integrating a small fraction of ranking data into the instruction-tuning blend of LLM, surprisingly works well on the evaluations of ranking associated with the RAG tasks, even surpassing the LLMs fine-tuned with 10× more ranking data due to its transferable design.

5. The proposed RankRAG method is extensively compared with several strong baselines, including the open-sourced ChatQA-1.5. Llama3-RankRAG significantly outperforms Llama3-ChatQA-1.5 and GPT-4 models on nine knowledge-intensive benchmarks. Furthermore, 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.

6. Retrieval-augmented generation (RAG) has been established for knowledge-intensive NLP tasks, and recent research has focused on aligning retrievers to the needs of LLMs for generation, designing multi-step retrieval processes, or filtering irrelevant contexts to improve the RAG pipeline. Furthermore, several studies have designed instruction-tuning methods dedicated to enhancing the search and RAG capability of LLMs.

7. The proposed RankRAG method instruction-tunes the LLM to simultaneously capture the relevance between the question and context and utilize the retrieved context for answer generation. The method involves a two-stage instruction-tuning framework for both context ranking and answer generation.

8. RankRAG significantly outperforms the existing RAG methods on nine general-domain and five biomedical benchmarks for RAG. The model excels especially on more challenging datasets such as long-tailed QA and multi-hop QA tasks, demonstrating its capability to enhance performance in challenging OpenQA datasets where top documents from retrievers are less relevant to the answer.

9. RankRAG demonstrates larger improvement on more challenging datasets and is data-efficient, achieving effective performance with a modest amount of ranking data and maintaining adaptability across various tasks. Furthermore, RankRAG is robust to the choice of retrievers and is time-efficient, with reranking improving the exact match score by 5.9% to 9.1% across different N settings without major increases in time. The proposed RankRAG method is ready for application to new domains without extra post-training, and its data efficiency and adaptability make it a strong model for diverse NLP tasks.

Summary

The paper introduces the RankRAG framework, which instruction-tunes a single language model for context ranking and answer generation in retrieval-augmented generation (RAG). Large language models (LLMs) in RAG typically utilize the top-k contexts from a retriever in retrieval-augmented generation. RankRAG is proposed to address the limitations of the current RAG pipeline, such as the limited capacity of the retriever, the trade-off of picking top-k contexts, and the need for an effective ranking model.

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. The effectiveness of instruction-tuned LLMs in context ranking and answer generation is demonstrated through the comparison of Llama3-RankRAG with other models, including GPT-4-0613, GPT-4-turbo-2024-0409, and ChatQA-1.5. Llama3-RankRAG significantly outperforms Llama3-ChatQA-1.5 and GPT-4 models on nine knowledge-intensive benchmarks. In addition, it 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.

High-Recall Context Extraction and Content Generation
By using a single language model for both ranking and answer generation, RankRAG achieves high-recall context extraction and high-quality content generation. The framework is readily applicable to diverse knowledge-intensive NLP tasks and demonstrates the capability to filter out irrelevant contexts during both the retrieval and generation phases of RAG. The paper proposes RankRAG as a novel framework that enhances LLM’s RAG capability through simultaneous instruction on context ranking and answer generation. The authors demonstrate that integrating a small fraction of ranking data into the instruction-tuning blend of LLM works surprisingly well on the evaluations of ranking associated with the RAG tasks, even surpassing the LLMs fine-tuned with 10x more ranking data.

RankRAG significantly outperforms existing RAG methods and demonstrates larger improvement on more challenging datasets. The paper presents comprehensive experiments on a variety of knowledge-intensive NLP tasks to demonstrate the zero-shot capabilities of RankRAG. It also discusses the effectiveness of RankRAG on biomedical benchmarks, demonstrating its ability to excel at medical QA tasks, even without fine-tuning on the biomedical domain. Furthermore, the paper illustrates the data efficiency of RankRAG, as it achieves effective performance with a modest amount of ranking data and maintains adaptability across various tasks. The authors also consider the efficiency of RankRAG, studying the performance versus time efficiency for RankRAG and finding that it outperforms the baseline model without reranking even with a lower selection of contexts for retrieval.

In summary, the paper introduces RankRAG as a state-of-the-art framework for context ranking and answer generation in retrieval-augmented generation, demonstrating its significant improvements over existing RAG methods and its potential for diverse knowledge-intensive NLP tasks.

Reference: https://arxiv.org/abs/2407.02485v1