RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs

Summary

This paper proposed a method about how to use a single LLM for both context reranking and answer in RAG tasks. Particularly, they finetuned a LLM with both ranking data and QA data with two stages. For inference, they use the trained llm to sample the top_k contexts at first, and then input them into llm to get the answer. Compared with normal rag workflow, this method choose the contexts by llm itself. Experiments in some QA tasks evaluated the QA effectiveness of this LLM.

Strengths

1. Two stages of training enhance the reranking capacity of llm using fewer ranking data. 2. Using just a single llm for context ranking and answer at the same time.

Weaknesses

1. The motivation is unclear. In lines 23 to 31, limitations 1 and 2 precisely explain the need for a reranker, which is also a challenge related to retrieval. Only with limited text in limitation 3 briefly mentions why the LLM is used for reranking contexts due to zero-shot performance, which has been proved with other rerankers in this paper. It is unclear about why using llm reranking itself rather than a separate reranker, maybe including the semantic gap between the reranker and llm or the larger length of reranking input at once. 2. Insufficient experiments in same reranking setting. It is lack of baselines with RAG methods also using a reranker, which can prove the core reranking effectiveness of RankRAG, such as rankgpt(Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents) and rankvicuna(RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models). And also, the amount of top-k contexts after reranking is also hard to find in Table 2&6. How many contexts for reranking are input into llm at once? In addition, there are some blank spaces in Table 2 that are not convincing. 3. Incremental techniques. Compared with ChatQA, RankRAG introduced the new training data RQA and RAR in stage 2th and reranking phrase during inference. But as shown in ablation study, each component contribute a little.

Questions

1. Can you analyze the relationship between high retrieval call and QA performance in RankRAG for limitation2 on line 26? Can you provide further analysis to demonstrate that your method can solve this limitation? 2. Have you conducted experiments to test whether RankRAG's reranking performance is robust when using different retrievers on lines 300?

Rating

5

Confidence

4

Soundness

2

Presentation

3

Contribution

2

Limitations

No potential negative societal impacts.

Summary

The authors introduce a novel approach to instruction fine-tuning large language models (LLMs) for ranking and answer generation tasks. Their approach involves two main steps: 1. Supervised Fine-Tuning: Initially, the LLM is fine-tuned on a general instruction-following dataset. 2. Ranking Task Fine-Tuning: The LLM is then further fine-tuned using a mix of different instruction ranking datasets that contains multiple ranking-oriented tasks. Incorporating ranking-based data during fine-tuning enhances the LLM's ability to rank documents retrieved through retrieval-augmented generation (RAG). The fine-tuned LLM is then used to rank the RAG-retrieved documents, and only the top-k ranked documents are added in the context. The authors demonstrate that their method significantly improves the LLM's performance for knowledge intensive RAG based tasks.

Strengths

1. The authors introduce a novel method of fine-tuning LLMs for combined ranking and answer generation tasks. 2. The proposed method outperforms existing approaches on RAG benchmarks, especially on challenging datasets where RAG retrieval is suboptimal, due to the LLM-based re-ranking. 3. Their model exceeds the performance of re-ranking models trained on much larger datasets. 4. The fine-tuned LLM demonstrates strong generalization capabilities, showcased by its performance on medical benchmarks. 5. The paper is well-written and includes exhaustive experiments.

Weaknesses

1. Scoring each document individually increases the latency significantly. Have you considered scoring multiple documents simultaneously? Similar to retrieval-augmented ranking dataset, you could input a group of documents and score them in a single pass. The group size could be tunable, balancing performance drop against latency improvement. 2. It would be helpful to include examples where RankRAG fails, particularly in cases where relevant documents are not ranked higher. Providing these examples can help understand scenarios where it might not perform optimally.

Questions

N/A

Rating

8

Confidence

4

Soundness

4

Presentation

4

Contribution

4

Limitations

The authors have acknowledged the limitations, specifically in terms of latency and its lack of training on code or mathematical data.

Summary

This work proposes an effective approach that enhances existing RAG methods by introducing an additional context refinement step. This step filters out retrieved, but non-relevant contexts prior to including them as context in the input for answer generation. The authors train context reranking alongside answer generation using a single LLM. They demonstrate that adding only a fraction of ranking data to the training blend yields superior ranking performance compared to training the same model in isolation on the ranking data, while outperforming other models that have been trained on 10 times more ranking data. For evaluation, the authors compare their method on 9 general- and 5 specific-domain datasets, showing consistent improvements across LLM sizes for the Llama model family (Llama 2 and 3) over existing methods.

Strengths

- The paper introduces the novel idea of using the same LLM to first assess the relevance of individual contexts in a cross-encoder ranking style before using them as input for answer generation. - The proposed RankRAG outperforms existing methods on various general and specific-domain datasets. - Extensive experimentation and ablations covers a wide range of possible setups including LLM size, retriever, and efficiency and effect of different model components.

Weaknesses

Main concern: - Reranking contributes only around 5% of the overall effectiveness on average (Table 4 RankRAG compared to Llama3-ChatQA-1.5-X), and the 7x computational overhead in inference time raises questions about its realistic application, given the computational demands of large models already without ranking contexts. The ratio of performance gained ( in combination with no sig. testing) and increase in computation is my main issue with this work. I am aware reranking fewer contexts decreases performance, however, similarity decreases performance gain over other methods. - No significance testing was done (for both generation on ranking ) to strengthen effectiveness claims as differences for most datasets are minor. Authors justify in their additionally provided checklist that sig. testing is not needed as generation and ranking is deterministic, this however, touches upon a different aspect and does not remove the need for testing whether the performance is significantly better than previous methods. Further, improvements in Table 2 stem from NQ and PopQA which are relatively simple datasets that can be answered with a single context, therefore it is not apparent why RankRAG would particularly excel at those. Moreover, the ranking performance for these datasets in Table 6 only marginally improves over other rerankers, therefore it is to be expected to obtain similar gains when replacing the RankRAG reranker module with other strong rerankers. As a side note averaging over different metrics - even though seen in many recent works - should not be done. Other points to improve upon: - No information about the crowdsourced dataset is provided. - In Section 4.2.3, the claim that LLM needs to be robust against irrelevant context contradicts the proposal to filter out irrelevant context beforehand. Some experimental setups are unclear: - It is not described how true/false tokens from context ranking are translated into a score that can be used for ranking. - In Section 5.1, it is not clear if baselines use different retrieval setups; otherwise, effectiveness claims do not seem valid. - It is unclear which number k is eventually used in RankRAG. The paper mentions optimizing for k=5,10,20, the baselines. - The related work section could mention GRITLM as the first model jointly training answer generation and ranking for RAG: Muennighoff, Niklas, et al. "Generative representational instruction tuning." arXiv preprint arXiv:2402.09906 (2024). Issues in Writing: - Line 332: "Observe that even with N = 20, We noted that" – incomplete sentence. - Line 41: "in RAG framework" -> "in the RAG framework". - Caption Fig 1: "the strongest RAG model," -> "the strongest RAG models,". - Line 100: "embedding model" -> "embedding models". - Line 111: "As illurstraded" -> "As illustrated". - Line 112: "one of the strongest model." -> "one of the strongest models". - Line 157: "that, it is" -> "that it is". - Line 165: "The LLM need" -> "The LLM needs". - Line 169: "ranking data are" -> "ranking data is".

Questions

- Why is a fixed number of contexts used? Some questions might need more contexts such as multi-hop datasets while others such as NQ (single context dataset) would need less. Instead of a score cutoff, why not use a dynamic number of contexts that is determined by the binary true/false label the mode already outputs? - In Section 5.4, the paper states that it incurs an additional 1.8× to 7.0× increase in time, significantly less than the 20× to 100× increase one might expect. It is not sufficiently explained why one would expect a 100x increase in time. - The approach for reranking context looks at passages individually, but retrieval training included listwise ranking. Was listwise ranking also tried for context reranking?

Rating

8

Confidence

5

Soundness

4

Presentation

3

Contribution

3

Limitations

The authors addressed the limitations adequately, however, could emphasize more on the dramatic inference time increase that results from the reranking step.