Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting

## Abstract

Abstract Retrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date responses. Recent RAG advancements focus on improving retrieval outcomes through iterative LLM refinement or self-critique capabilities acquired through additional instruction tuning of LLMs. In this work, we introduce Speculative RAG – a framework that leverages a larger generalist LM to efficiently verify multiple RAG drafts produced in parallel by a smaller, distilled specialist LM. Each draft is generated from a distinct subset of retrieved documents, offering diverse perspectives on the evidence while reducing input token counts per draft . This approach enhances comprehension of each subset and mitigates potential position bias over long context . Our method accelerates RAG by delegating drafting to the smaller specialist LM, with the larger generalist LM performing a single verification pass over the drafts. Extensive experiments demonstrate that Speculative RAG achieves state-of-the-art performance with reduced latency on TriviaQA, MuSiQue, PubHealth, and ARC-Challenge benchmarks. It notably enhances accuracy by up to 12.97% while reducing latency by 51% compared to conventional RAG systems on PubHealth.

## 1 Introduction

Large language models (LLMs) have demonstrated remarkable success in question answering tasks (Brown et al., ; Achiam et al., ; Team et al., ). Trained on massive datasets, LLMs leverage their extensive parametric memory to generate seemingly plausible responses to user queries (Kojima et al., ; Kamalloo et al., ). However, when faced with knowledge-intensive questions demanding up-to-date information or obscure facts (Petroni et al., ), LLMs can struggle with factual inaccuracies and produce hallucinated content (Huang et al., ; Xu et al., ).

Retrieval Augmented Generation (RAG) has emerged as a promising solution to mitigate these issues. By incorporating information retrieved from an external database into the context (Gao et al., ), RAG effectively reduces factual errors in knowledge-intensive tasks. This approach not only enables easy and efficient access to vast databases but also facilitates timely and accurate knowledge integration
Due to the inherent limitations in the precision of current dense retrievers and the vastness of knowledge required to answer complex questions (Chen et al., ), RAG systems typically retrieve multiple documents to ensure the inclusion of all necessary information in the context (Petroni et al., ). This practice inevitably increases the length of the input to the LLMs, presenting significant challenges, particularly since encoding lengthy retrieved documents incurs additional latency and require more complex reasoning.
Recent studies have explored ways to extend the context length limit of LLMs (Ding et al., ; Reid et al., ; Ma et al., ), yet achieving well-grounded reasoning over extended contexts remains an open question (Liu et al., ; Li et al., ). Consequently, striking a balance between efficiency and effectiveness in RAG has become a central research question in the literature.
Existing work on RAG systems primarily concentrates on improving the quality of contextual information in retrieval outcomes, but often neglecting the latency issues associated with these systems (Ma et al., ; Baek et al., ; Yan et al., ; Xie et al., ; Asai et al., ; Feng et al., ).
These methods typically rely on multiple refinement iterations or customized instruction-tuning for self-critique abilities. Integrating such enhancements into generic LMs requires additional training or increased latency, posing practical challenges in real-world applications.

Figure: Figure 1: Illustration of different RAG approaches. Given a knowledge-intensive query $Q$ and retrieved documents, (a) Standard RAG incorporates all documents into the prompt, increasing input length and slowing inference; (b) Self-Reflective RAG (Asai et al., ) requires specialized instruction-tuning of the general-purpose language model (LM) to generate specific tags for self-reflection; (c) Corrective RAG (Yan et al., ) employs an external retrieval evaluator to refine document quality, focusing solely on contextual information without enhancing reasoning capabilities; (d) In contrast, our proposed Speculative RAG leverages a larger generalist LM to efficiently verify multiple RAG drafts produced in parallel by a smaller, specialized LM. Each draft is generated from a distinct subset of retrieved documents, providing diverse perspectives on the evidence while minimizing the number of input tokens per draft.
Refer to caption: x1.png

To this end, we introduce Speculative RAG, a RAG framework designed to offload computational burden to a smaller, specialist LM that serves as an efficient and robust RAG module for existing generalist LMs. Inspired by Speculative Decoding (Leviathan et al., ; Chen et al., ; Xia et al., ), which accelerates auto-regressive LM inference by concurrently generating multiple draft tokens with a smaller model and verifying them in parallel with the base model, our approach adapts this concept to RAG.

In Speculative RAG, we partition retrieved documents into subsets for drafting answer candidates. We cluster the retrieved documents by content similarity and sample one document from each cluster to form a subset, minimizing redundancy and maximizing diversity. These document subsets are then fed to multiple instances of the RAG module, which generate draft answers with corresponding rationales in parallel. This smaller, specialized RAG module, excels at reasoning over retrieved documents and can rapidly produce accurate responses. Subsequently, the generalist LM bypasses the detailed review of potentially repetitive documents, focusing instead on validating the drafts against the rationales to determine the most accurate answer. We utilize the strong language modeling capabilities of generalist LMs, calculating the conditional generation probability of the answer drafts and rationales as a confidence score. Our key contributions are:

- •
We introduce a novel RAG framework that employs a smaller specialist RAG drafter to generate high-quality draft answers. Each draft is derived from a distinct subset of retrieved documents, offering diverse perspectives while reducing input token counts per draft.
- •
The generalist LM, operating with the RAG drafter, requires no additional tuning. It simply verifies and integrates the most promising draft into the final answer. This approach enhances comprehension of each subset and mitigates potential lost-in-the-middle (Liu et al., ) phenomenon.
- •
Our method significantly accelerates RAG by delegating drafting to the smaller specialist LM, with the larger generalist LM performing a single, unbiased verification pass over the drafts in parallel. Extensive experiments on 4 free-form question-answering and closed-set generation benchmarks demonstrate the superior effectiveness and efficiency of the method.

## 2 Related Works

Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) enhances LLMs by retrieving relevant documents from external databases and incorporating them into the generation process (Gao et al., ; Lewis et al., ; Khandelwal et al., ; Izacard & Grave, ; Luo et al., ). Recent work has primarily focused on enabling LLMs to understand when and what to retrieve (Ma et al., ; Chen et al., ; Jiang et al., ; Schick et al., ), or designing approaches to better utilize contexts (Yu et al., ; Yoran et al., ; Wang et al., ; Sarthi et al., ; Baek et al., ; Xu et al., ; Kim et al., ). Among them, SAIL (Luo et al., ) fine-tunes a pre-trained LLM on web search data to filter irrelevant contents. Self-Reflective RAG (Asai et al., ) introduces reflection tokens to guide retrieval and annotation in instruction-tuning datasets. However, both approaches require additional instruction-tuning of generic LLMs, which is resource-intensive and may lead to forgetting or over-fitting (Luo et al., ). Furthermore, long context with retrieved documents can suffer from computational inefficiency and position bias (Liu et al., ). Corrective RAG (Yan et al., ) on the other hand proposes a lightweight retrieval evaluator, but it lacks the capability for high-level reasoning. In contrast, our proposed Speculative RAG addresses these limitations by leveraging a smaller RAG drafter model to efficiently understand diverse perspectives in retrieval results and generate drafts for the generalist LMs to verify and integrate.

Speculative Decoding
Speculative decoding (Stern et al., ; Xia et al., ; Chen et al., ; Leviathan et al., ; Xia et al., ) aims to reduce auto-regressive decoding latency through a draft-then-verify paradigm. This involves drafting multiple future tokens with a small model and verifying them in parallel with the target model (Xia et al., ). The draft model is typically either an independent model from the same series (Leviathan et al., ; Chen et al., ) or the target model itself (Zhang et al., ; Cai et al., ).
Our approach extends this concept from token-level drafting to answer-level drafting. In contrast to traditional verification criteria (Stern et al., ; Xia et al., ; Leviathan et al., ; Chen et al., ; Miao et al., ), which accept or reject tokens based on their generation probabilities, we leverage language modeling objectives to directly assess the confidence of entire answer drafts.

## 3 Speculative Retrieval Augmented Generation through Drafting

Problem Formulation
In knowledge intensive tasks, each entry can be represented as $(Q,D,A)$, where $Q$ is a question or statement that requires additional knowledge; $D=\{d_{1},...,d_{n}\}$ is a set of $n$ documents retrieved from the database; $A$ is the expected answer. Particularly, in question answering tasks, $Q$ and $A$ are the question and the expected answer in natural language form; in the statement verification tasks, $Q$ is a statement and $A\in\{\texttt{True},\texttt{False}\}$ is a Boolean value indicating the statement’s correctness; in the multiple choice tasks, $Q$ is a question with a few options and $A\in\{\texttt{A},\texttt{B},\texttt{C},...\}$ is the index of the correct answer. The objective of a RAG system is to generate a fluent response containing the expected answer or select the expected answer from the provided options based on the context provided by the retrieved supporting documents.

### 3.1 Overview

We introduce Speculative Retrieval Augmented Generation (Speculative RAG), as illustrated in Figure 1. We aim at enhancing the reasoning ability of LLMs over retrieved documents without compromising processing speed.
Instead of relying on brute-force parameter scaling or instruction-tuning an entire LM to handle knowledge-intensive tasks, we propose a divide-and-conquer approach. We utilize a smaller specialist LM, the RAG drafter, to rapidly generate multiple answer drafts based on retrieved results. Then, a larger generalist LM, the RAG verifier, assesses these drafts, selects the best one based on its rationale, and integrates it into the generation results.

Figure: Algorithm 1 Speculative RAG

Specifically, as shown in Algorithm 1, we first cluster the retrieved documents with regard to their relation to the posed question, where each cluster represents one perspective in the retrieval results (Line 1). Then we sample one document from each cluster into a subset so the documents in this subset covers the multiple perspectives in the retrieval results. We aim at minimizing redundancy and increase the diversity of the documents (Line 1 to 1).
We denote one subset as $\bm{\delta}\subset D$ that contains retrieved documents with diverse contents and multiple perspectives in the retrieval results. Then, we distribute each subset $\bm{\delta}$ to a RAG drafter endpoint $\mathcal{M}_{\text{Drafter}}$ with the posed question $Q$ to generate the answer draft $\alpha$ and the rationale $\beta$ in parallel (Line 1). The RAG drafter is instruction-tuned to be a specialist in understanding the retrieved documents and produce rationales that are faithful to the input documents. It is smaller than generalist LMs, and its parallel processing further ensures high efficiency. For each draft-rationale pair $(\alpha,\beta)$ from $\mathcal{M}_{\text{Drafter}}$, we compute a confidence score with the generalist LM $\mathcal{M}_{\text{Verifier}}$ based on the question $Q$ and corresponding rationale $\beta$ (Line 1). It is worth mentioning that $\mathcal{M}_{\text{Verifier}}$ does not need to be instruction-tuned since we leverage its language modeling ability already learned during pre-training. Meanwhile, $\mathcal{M}_{\text{Verifier}}$ can verify the drafts based on the informative rationale provided by $\mathcal{M}_{\text{Drafter}}$ instead of processing tedious or possibly redundant retrieved documents.
Finally, we select the answer draft with the highest confidence score as the final answer and integrate it into the generation results of the generalist LM (Line 1).

### 3.2 Specialist RAG Drafter

Instead of tuning a large generalist LM for the RAG scenario, we leverage a smaller specialist LM, $\mathcal{M}_{\text{Drafter}}$, to understand retrieved documents. $\mathcal{M}_{\text{Drafter}}$ is specialized in answering the given question based on the supporting documents and not expected to cope with general problems. It serves as a RAG module for the generalist LMs when solving knowledge-intensive tasks. We train $\mathcal{M}_{\text{Drafter}}$ to generate both the answer draft and the rationale to better understand the contextual documents.

Instruction Tuning  Given a triplet $(Q,A,D)$, where $Q$ is a general query, $A$ is the response, and $D$ is a retrieved supporting document, we augment it with the rationale of the response $A$ based on the document $D$. We denote the rationale as $E$ which extracts essential information from the document and explains why the response is reasonable to the query concisely (Hsieh et al., ) so it is of shorter length and delivers information coherent with the original document. We leverage relatively strong LMs to automatically synthesize the rationale $E$ for each triplet. Specifically, we directly query the strong LM to understand the knowledge from the document and provide the intermediate rationale between the instruction and response. Refer to Appendix A for detailed prompts. After generating the rationale, we finetune a pre-trained LM using the standard language modeling objective, maximizing the likelihood: $\mathbb{E}_{(Q,A,D,E)}\log P_{\mathcal{M}_{\text{Drafter}}}(A,E\mid Q,D)$,
where $(Q,A,D,E)$ is an augmented entry in the dataset; $P_{\mathcal{M}_{\text{Drafter}}}(A,E\mid Q,D)$ is the probability of generating the response and rationale based on the query and document.
We use this instruction-tuned model as the specialist RAG drafter which learns to generate a well-grounded response and rationale given the query and relevant documents.

Multi-Perspective Sampling  For each knowledge-intensive question, we retrieve a set of documents from the database using the posed question as the retrieval query. These documents may contain diverse content due to the ambiguity inherent in the query. To minimize redundancy and enhance diversity of the document subsets used for generating answer drafts, we employ a multi-perspective sampling strategy. We first cluster the documents into a few topics using an instruction-aware embedding model (Peng et al., ) and the K-Means clustering algorithm (Jin & Han, ).

$$ $\displaystyle\texttt{emb}(d_{1}),...,\texttt{emb}(d_{n})=\mathcal{E}(d_{1},... ,d_{n}|Q)$ $\displaystyle\{\bm{c}_{1}$ $\displaystyle,...,\bm{c}_{k}\}=\texttt{K-Means}(\texttt{emb}(d_{1}),..., \texttt{emb}(d_{n}))$ $\displaystyle\bm{\delta}=\left\{\texttt{random.sample}(\bm{c})\text{ for }\bm{ c}\in\{\bm{c}_{i}\}_{1}^{k}\right\}$ $$

where $\mathcal{E}$ is an instruction-aware embedding model which embeds a string with regard to a provided instruction (the posed question $Q$); $\texttt{emb}(d_{i})$ is the embedding for the retrieved document $d_{i}$; $\bm{c}_{j}$ is a cluster of retrieved documents with similar topics and contents; $k$ is a hyper-parameter that controls the number of clusters. We sample one document from each cluster into a document subset $\bm{\delta}$ so each subset contains $k$ documents of diverse contents. In total, we construct $m$ subsets for parallel inference with the RAG drafter.

RAG Drafting  We run $\mathcal{M}_{\text{Drafter}}$ over the $m$ document subsets and produce corresponding answer drafts. Refer to Appendix B for detailed prompt. We incorporate each document subset into the prompt and query $\mathcal{M}_{\text{Drafter}}$ for responses. We obtain $m$ drafts as the answer candidates and each draft is grounded based on the multiple perspectives in the retrieval results. Specifically, given a document subset $\bm{\delta}_{j}=\{d_{j_{1}},..,d_{j_{k}}\}$, we query $\mathcal{M}_{\text{Drafter}}$ in parallel with the following prompt for the answer draft and rationale:
$Q,d_{j_{1}},...,d_{j_{k}}\to\alpha_{j},\beta_{j}$,
where the prompt contains the posed question $Q$ along with the document subset; the generation result contains the answer draft $\alpha$ and the rationale $\beta$. We denote the conditional generation probability as $\rho_{\text{Draft},j}=P(\beta_{j}|Q,d_{j_{1}},...,d_{j_{k}})+P(\alpha_{j}|Q,d_
{j_{1}},...,d_{j_{k}},\beta_{j})$, which measures the reliability of generating rationales and the confidence in producing answer drafts.

### 3.3 Generalist RAG Verifier

After generating drafts and the rationale from the RAG drafter $\mathcal{M}_{\text{Drafter}}$, we evaluate them by a generalist LM $\mathcal{M}_{\text{Verifier}}$ to filter out the less reliable drafts and select the best answer. The generalist LM can be any off-the-shelf pre-trained LM. We only consider the draft-rationale pair $(\alpha,\beta)$ and skip the tedious and redundant retrieval results. We resort to the language modeling ability of the generalist LM to rank and select the draft-rationale pairs.

Evaluation Scores
First, we calculate the self-consistency score by determining the conditional probability of generating a draft-rationale pair given the question, $\rho_{\text{Self-contain}}=P(\alpha,\beta|Q)$. This score helps assess whether the draft and rationale are self-consistent in the context of the question. Given the characteristics of language modeling, a self-consistent draft-rationale pair is expected to yield a higher probability.
Furthermore, we incorporate a self-reflection statement $R$ that prompts $\mathcal{M}_{\text{Verifier}}$ to assess the reliability of an answer draft (e.g. “Do you think the rationale supports the answer, yes or no?”). We define the self-reflection score as $\rho_{\text{Self-reflect}}=P(\texttt{"Yes"}|Q,\alpha,\beta,R)$ where we compute the conditional probability of the positive answer ("Yes") to the self-reflection statement.

Computation Method  We can efficiently compute the self-consistency and self-reflection scores within one forward pass of $\mathcal{M}_{\text{Verifier}}$. Given a question $Q$ and a draft-rationale pair $(\alpha,\beta)$, we construct a prompt $[Q,\alpha,\beta,R,\texttt{"Yes"}]$, where $R$ is the self-reflection statement. We encode the prompt with $\mathcal{M}_{\text{Verifier}}$, and acquire the probability of each token conditioned on the previous tokens $P(t_{i}|t_{<i})$. We leverage this auto-regressive feature and aggregate the probability of the relevant tokens to compute the self-consistent score $\rho_{\text{Self-contain}}$ and self-reflection score $\rho_{\text{Self-reflect}}$.

$$ $\displaystyle\underrightarrow{Q,\overbrace{\alpha,\beta}^{\rho_{\text{SC}}},R, \overbrace{\texttt{"Yes"}}^{\rho_{\text{SR}}}}\ \Rightarrow\begin{cases}\rho_{ \text{SC}}=\prod_{t_{i}\in\alpha}P(t_{i}|t_{<i})\cdot\prod_{t_{i}\in\beta}P(t_ {i}|t_{<i})\\ \rho_{\text{SR}}=\prod_{t_{i}\in\texttt{"Yes"}}P(t_{i}|t_{<i})\end{cases}$ $$

Finally, we produce the final score, $\rho_{j}=\rho_{\text{Draft},j}\cdot\rho_{\text{SC},j}\cdot\rho_{\text{SR},j}$, and then select the most reliable answer as the final answer to the question $\hat{A}=\arg\max_{\alpha_{j}}\rho_{j}$.

## 4 Experiments

We evaluate our proposed Speculative RAG on four public retrieval augmented generation benchmarks: TriviaQA (unfiltered) (Joshi et al., ), MuSiQue (Trivedi et al., ), PubHealth (Zhang et al., ), and ARC-Challenge (Clark et al., ). TriviaQA and MuSiQue are challenging open-domain question answering datasets where RAG systems are required to answer questions on factual knowledge. TriviaQA typically requires one accurate piece of evidence from the documents, whereas MuSiQue demands multiple documents to construct a multi-hop reasoning chain. Following previous works (Guu et al., ; Asai et al., ; Yan et al., ), we evaluate performance of the free-form generation based on whether gold answers are contained within the generated response or not. PubHealth and ARC-Challenge are closed-set generation datasets. PubHealth is a dataset of medical claims spanning a variety of biomedical subjects and it requires the RAG system to verify a given claim based on the retrieved documents. ARC-Challenge introduces a multi-choice question answering dataset, composed of science exam questions from grade 3 to grade 9. For closed-set generation tasks, we use accuracy metrics to evaluate whether the generated answers match the ground truth.

### 4.1 Baselines

Standard RAG
For standard RAG, we incorporate all the retrieved documents into the prompt as contextual information. Refer to Appendix C for detailed prompts. We run standard RAG experiments on off-the-shelf LLMs including
Mistral${}_{\text{7B}}$, Mistral-Instruct${}_{\text{7B}}$ (Jiang et al., ), Mixtral${}_{\text{8x7B}}$, Mixtral-Instruct${}_{\text{8x7B}}$ (Jiang et al., ), and Alpaca${}_{\text{7B}}$ (Dubois et al., ). We also include the performance of Toolformer (Schick et al., ) and SAIL (Luo et al., ) which are originally reported from Asai et al. (). Toolformer${}_{\text{7B}}$ is an LM instruction-tuned to use tools including a search engine, and SAIL${}_{\text{7B}}$ is an LM instruction-tuned on the Alpaca instruction tuning set augmented with search results from different sources such as DuckDuckGo and Wikipedia.

Self-Reflective RAG and Corrective RAG
Self-Reflective RAG (Self-RAG)  (Asai et al., ) and Corrective RAG (CRAG)  (Yan et al., ) are more advanced RAG systems that enhances the quality of contextual information in the retrieval results.
CRAG introduces an external evaluator to assess the quality of retrieved documents, and to refine them before the response generation. Self-RAG instruction-tunes an LM to generate special self-refection tags. These tags guides the LM to dynamically retrieve documents when necessary, critique the retrieved documents relevance before generating responses. Self-CRAG is to apply the Self-RAG approach on the refined documents of CRAG. We adopt the same backbone LLMs across all methods as our proposed Speculative RAG for fair comparisons.

### 4.2 Experiment Settings

In our experiments, we utilize Mistral${}_{\text{7B}}$ (v0.1) as our base LM for the RAG drafter. For RAG verifier, we employ either Mistral${}_{\text{7B}}$ (v0.1) or Mixtral${}_{\text{8x7B}}$ (v0.1) without any fine-tuning, denoted as $\mathcal{M}_{\text{Verifier-7B}}$ or $\mathcal{M}_{\text{Verifier-8x7B}}$. We pre-compute embeddings of retrieved documents using a lightweight instruction-aware embedding model InBedder${}_{\text{Roberta}}$ (Peng et al., ) as part of the retrieval process. Inference is conducted using the vLLM framework (Kwon et al., ) with greedy decoding (temperature = 0). We adopt the same experiment settings from Asai et al. () and include a more challenging benchmark, MuSiQue (Trivedi et al., ). Our focus is on RAG reasoning rather than evidence citation, so we omit the other two long-form generation benchmarks, Biography (Min et al., ) and ALCE-ASQA (Gao et al., ).
On TriviaQA, PubHealth, and ARC-Challenge, we retrieve top 10 documents and generate 5 drafts per query ($m=5$), with each draft based on a subset of 2 documents ($k=2$). For the MuSiQue dataset, we retrieve top 15 documents and generate 10 drafts for each query ($m=10$), each using a subset of $6$ documents due to more complex reasoning. Further details regarding instruction-tuning can be found in Appendix E.

### 4.3 Main Results

**Table 1: Retrieval augmentation generation results on TriviaQA, MuSiQue, PubHealth, and ARC-Challenge (ARC-C). (^∗We use the RAG drafter’s generation probability $\rho_{\text{Draft}}$ as the confidence score for selecting drafts when we use it alone; ^† indicates numbers reported in Asai et al. (); $-$ denotes numbers that are not reported by the original papers or are not applicable; ^‡we use Mistral${}_{\text{7B}}$ or Mixtral${}_{\text{8x7B}}$ as the RAG verifier, and denote them as $\mathcal{M}_{\text{Verifier-7B}}$ or $\mathcal{M}_{\text{Verifier-8x7B}}$.)**
| RAG Method | Free-form | Closed-set |  |  |
| --- | --- | --- | --- | --- |
| TriviaQA | MuSiQue | PubHealth | ARC-C |  |
| Standard RAG |  |  |  |  |
| Mistral${}_{\text{7B}}$ (Jiang et al., ) | 54.15 | 16.71 | 34.85 | 42.75 |
| Mixtral${}_{\text{8x7B}}$ (Jiang et al., ) | 59.85 | 19.16 | 37.08 | 48.72 |
| Mistral-Instruct${}_{\text{7B}}$ (Jiang et al., ) | 67.11 | 17.99 | 42.15 | 47.70 |
| Mixtral-Instruct${}_{\text{8x7B}}$ (Jiang et al., ) | 73.91 | 29.42 | 63.63 | 78.41 |
| Alpaca${}_{\text{7B}}$ (Dubois et al., )^† | 64.1 | - | 40.2 | 48.1 |
| Toolformer${}_{\text{6B}}$ (Schick et al., )^† | 48.8 | - | - | - |
| SAIL${}_{\text{7B}}$ (Luo et al., )^† | - | - | 69.2 | 48.4 |
| Self-Reflective RAG & Corrective RAG |  |  |  |  |
| CRAG${}_{\text{Mistral-7B}}$ (Yan et al., ) | - | - | 59.04 | 74.87 |
| Self-RAG${}_{\text{Mistral-7B}}$ (Asai et al., ) | 64.84 | 21.72 | 72.44 | 74.91 |
| Self-CRAG${}_{\text{Mistral-7B}}$ (Yan et al., ) | - | - | 72.85 | 75.26 |
| Our Speculative RAG |  |  |  |  |
| $\mathcal{M}_{\text{Drafter-7B}}$^∗ | 71.11 | 27.89 | 75.58 | 74.49 |
| $\mathcal{M}_{\text{Verifier-7B}}$^‡ + $\mathcal{M}_{\text{Drafter-7B}}$ | 73.91 | 31.03 | 75.79 | 76.19 |
| $\mathcal{M}_{\text{Verifier-8x7B}}$^‡ + $\mathcal{M}_{\text{Drafter-7B}}$ | 74.24 | 31.57 | 76.60 | 80.55 |

We compare Speculative RAG with standard RAG approaches, as well as the more advanced Self-Reflective RAG and Corrective RAG on four datasets: TriviaQA, MuSiQue, PubHealth, and ARC-Challenge. We report the performance of $\mathcal{M}_{\text{Drafter-7B}}$ when used alone or paired with the RAG verifier (e.g. $\mathcal{M}_{\text{Verifier-7B}}$, $\mathcal{M}_{\text{Verifier-8x7B}}$).
Following prior work (Asai et al., ; Yan et al., ), we report accuracy as the performance metric.

Superior Performance over Baselines  Table 1 demonstrates that Speculative RAG consistently outperforms all baselines across all four benchmarks. Particularly, $\mathcal{M}_{\text{Verifier-8x7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ surpasses the most competitive standard RAG model, Mixtral-Instruct${}_{\text{8x7B}}$, by 0.33% on TriviaQA, 2.15% on MuSiQue, 12.97% on PubHealth, and 2.14% on ARC-Challenge. With a comparable number of instruction-tuned parameters, $\mathcal{M}_{\text{Verifier-7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ outperforms all Self-Reflective and Corrective RAG methods, and $\mathcal{M}_{\text{Drafter}}$ alone can surpass these baselines in most settings.

Effective Instruction Tuning for RAG Drafter  Our instruction tuning is effective in enhancing the reasoning ability of the drafter model (Hsieh et al., ), as we observe a remarkable performance improvement comparing Mistral${}_{\text{7B}}$ and $\mathcal{M}_{\text{Drafter-7B}}$. Moreover, the performance of Mixtral${}_{\text{8x7B}}$ significantly improves when paired with the instruction-tuned RAG drafter $\mathcal{M}_{\text{Drafter-7B}}$, showing gains of 14.39% on TriviaQA, 12.41% on MuSiQue, 39.52% on PubHealth, and 31.83% on ARC-Challenge. Similar improvements are observed with Mistral${}_{\text{7B}}$ as well. For Mistral${}_{\text{7B}}$, we observed improvements of 19.76% on TriviaQA, 14.32% on MuSiQue, 40.94% on PubHealth, and 33.44% on ARC-Challenge. We attribute these improvements to the superior reasoning capabilities of the RAG drafter over the retrieved documents in Speculative RAG. By minimizing the redundancy in the sampled documents, the RAG drafter generates higher quality answer drafts based on diverse perspectives from the retrieval results.

Reliable Scoring by RAG Verifier  The reliable draft verification by the generalist LM also contributes to the enhanced performance. The performance improves remarkably comparing $\mathcal{M}_{\text{Drafter-7B}}$ and $\mathcal{M}_{\text{Verifier-7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$. The instruction-tuned RAG drafter is specialized in generating answer drafts based on the retrieved documents while the language modeling capabilities of generic LMs are leveraged to validate each draft in light of its rationale. This method is both effective and easy to implement, showcasing the effectiveness of this verification approach.

### 4.4 Latency Analysis

We analyze the latency of Standard RAG and our Speculative RAG on TriviaQA, MuSiQue, PubHealth, and ARC-Challenge. We randomly sample 100 cases from each dataset and report the average time cost for each case, as shown in Figure 2. To simulate real-world application scenarios, we process cases individually without batching.
As representative example, we run $\mathcal{M}_{\text{Verifier-8x7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ for Speculative RAG and Mixtral-Instruct${}_{\text{8x7B}}$ for Standard RAG, as these demonstrate the highest performance among competitive baselines (see Table 1). We launch 5 endpoints of $\mathcal{M}_{\text{Drafter-7B}}$ for parallel drafting on TriviaQA, PubHealth, and ARC-Challenge. We launch 10 endpoints for MuSiQue due to more drafts. We use tensor parallelism to fit Mixtral-Instruct${}_{\text{8x7B}}$ into the GPU memory. We report the latency of Mixtral-Instruct${}_{\text{8x7B}}$ under tensor parallelism sizes of 4, 8, 16. Increasing tensor parallelism does not improve efficiency due to overheads in tensor aggregation and communication. In contrast, Speculative RAG, with its smaller RAG drafter and parallel draft generation, consistently achieves the lowest latency across all datasets.
Particularly, it reduces latency by up to 23.41% on TriviaQA, 17.28% on MuSiQue, 51.25% on PubHealth, and 26.73% on ARC-Challenge.
This highlights the advantage of our approach in reducing processing time while maintaining high performance.

Figure: Figure 2: Latency analysis of Standard RAG: Mixtral-Instruct${}_{\text{8x7B}}$ with tensor parallelism and Speculative RAG: $\mathcal{M}_{\text{Verifier-8x7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ on TriviaQA, MuSiQue, PubHealth, and ARC-Challenge. The latency difference between Standard RAG and Speculative RAG is highlighted in red (+$x$%). TP indicates the tensor parallelism size when running Mixtral-Instruct${}_{\text{8x7B}}$ for Standard RAG. The latency varies across different datasets due to different retrieved document lengths. Speculative RAG encodes the retrieved documents in parallel and generates answer drafts with a smaller RAG drafter. This significantly improves the efficiency over Standard RAG.
Refer to caption: x2.png

### 4.5 Ablation Studies

We conduct ablation studies on the key components of Speculative RAG during both drafting and verification stages on TriviaQA and PubHealth in Table 2. We use $\mathcal{M}_{\text{Verifier-8x7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ as a running configuration. Same as the main results, we report the accuracy as performance metrics.

Diversity and reduced redundancy in retrieval improves draft quality significantly.  In the first set of experiments, we evaluate the impact of multi-perspective sampling during the drafting. Recall that Speculative RAG clusters retrieved documents into distinct perspectives and sample one document from each cluster to reduce redundancy for the draft generation. We compare this against two alternative sampling strategies: (1) Random sampling without clustering, where we randomly select a document subset as context, and (2) Sampling from the same cluster, where we select all documents from a single cluster. Our results indicate that our proposed sampling method yields the best performance thanks to its ability to leverage diverse context. Particularly, it improves the accuracy up to 1.88% on TriviaQA and 2.23% on PubHealth. While random sampling without clustering introduces diversity, it is prone to including redundant documents, degrading draft quality. Sampling from the same cluster significantly underperforms due to a lack of diverse perspectives.

Scoring method on self-consistency and self-reflection refines draft quality effectively.
In the second set of experiments, we examine the scoring method during verification. We remove each of the specific confidence scores, $\rho_{\text{Draft}}$, $\rho_{\text{Self-contain}}$, or $\rho_{\text{Self-reflect}}$ in turn. Performance drops are observed when any score is removed. Particularly, removing $\rho_{\text{Draft}}$ leads to a minimal decline, 0.19% on TriviaQA and 1.12% on PubHealth, likely due to the limited verification capability of the smaller RAG drafter. Removing either $\rho_{\text{Self-contain}}$ or $\rho_{\text{Self-reflect}}$ results in similar performance decreases, around 2.0% on TriviaQA and around 0.8% on PubHealth, indicating that both self-containment and self-reflection capture different key aspects of reasoning and are crucial during verification. Random selection without verification leads to substantial underperformance, resulting in a performance decline of 5.69% on TriviaQA and 5.37% on PubHealth.

**Table 2: Ablation study of Speculative RAG in the drafting and verification stages on TriviaQA and PubHealth.**
|  | TriviaQA | PubHealth |
| --- | --- | --- |
| $\mathcal{M}_{\text{Verifier-8x7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ | 74.24 | 76.60 |
| Drafting Stage |  |  |
| Random sampling w/o clustering | 73.02 (-1.22) | 75.38 (-1.22) |
| Sampling from the same cluster | 72.36 (-1.88) | 74.37 (-2.23) |
| Verification Stage |  |  |
| w/o $\rho_{\text{Draft}}$ ($\rho=\rho_{\text{Self-contain}}\cdot\rho_{\text{Self-reflect}}$) | 74.05 (-0.19) | 75.48 (-1.12) |
| w/o $\rho_{\text{Self-contain}}$ ($\rho=\rho_{\text{Draft}}\cdot\rho_{\text{Self-reflect}}$) | 72.04 (-2.20) | 75.89 (-0.71) |
| w/o $\rho_{\text{Self-reflect}}$ ($\rho=\rho_{\text{Draft}}\cdot\rho_{\text{Self-contain}}$) | 72.36 (-1.88) | 75.68 (-0.92) |
| Random selection w/o verification | 68.55 (-5.69) | 71.23 (-5.37) |

**Table 3: Performance and latency analysis of Speculative RAG on TriviaQA and PubHealth using $\mathcal{M}_{\text{Verifier-8x7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$. We add the original document subset $\bm{\delta}$ to the context or replace the generated rationale $\beta$ with the original retrieved document subset $\bm{\delta}$ during verification, i.e. we compute the self-containment score as $\rho_{\text{Self-contain}}=P(\alpha,\bm{\delta}|Q)$ or $\rho_{\text{Self-contain}}=P(\alpha,\bm{\delta},\beta|Q)$, and compute the self-reflection score as $\rho_{\text{Self-reflect}}=P(\texttt{"Yes"}|Q,\alpha,\bm{\delta},R)$ or $\rho_{\text{Self-reflect}}=P(\texttt{"Yes"}|Q,\alpha,\bm{\delta},\beta,R)$, where $Q$ is the query; $\alpha$ is the answer draft; $R$ is the self-reflection statement.**
|  | TriviaQA | PubHealth |  |  |
| --- | --- | --- | --- | --- |
| Accuracy (%) | Latency (s) | Accuracy (%) | Latency (s) |  |
| $\mathcal{M}_{\text{Verifier-8x7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ |  |  |  |  |
| $\rho=\texttt{Score}(\alpha|Q,\beta)$ | 74.24 | 1.93 | 76.60 | 1.17 |
| $\rho=\texttt{Score}(\alpha|Q,\bm{\delta})$ | 74.08 (-0.16) | 2.13 (+10.36%) | 76.09 (-0.51) | 1.31 (+11.97%) |
| $\rho=\texttt{Score}(\alpha|Q,\beta,\bm{\delta})$ | 74.32 (+0.08) | 2.17 (+12.44%) | 76.29 (-0.31) | 1.33 (+13.68%) |

Figure: (a) We include 5, 10, 15, 20 drafts and sample 2 supporting documents for each draft.
Refer to caption: x3.png

### 4.6 Effects of Generated Rationale for Verification

In Speculative RAG, we utilize the generated rationale $\beta$ from the RAG drafter as an indicator of the trustworthiness of answer drafts $\alpha$. The rationales highlight relevant points, omit redundant information, and bridge logical gaps between drafts and their supporting documents.
To evaluate the effectiveness of the rationales, we create two alternative scoring methods: (a) replacing rationale with retrieved documents ($\rho=\texttt{Score}(\alpha|Q,\bm{\delta})$), or (b) adding retrieved documents to rationale ($\rho=\texttt{Score}(\alpha|Q,\beta,\bm{\delta})$). We compare these alternatives to the scoring method used in Speculative RAG ($\rho=\texttt{Score}(\alpha|Q,\beta)$) in Table 3.
The results show that incorporating longer retrieved documents does not consistently improve performance and tends to increase latency. This suggest that the generated rationale is already of high quality and serves as an effective bridge between the supporting documents and the generated answer drafts. By leveraging this rationale, we can efficiently verify drafts using a generic LM, leading to accurate final results.

### 4.7 Effects of Draft Number and Document Subset Size

We investigate the performance of Speculative RAG under varying numbers of drafts. Using $\mathcal{M}_{\text{Verifier-7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ with 5, 10, 15, 20 drafts on TriviaQA and PubHealth. We sample two documents as context per draft. The results are illustrated in Figure 3(a). Since we retrieve top 10 documents in total, we sample up to 20 drafts in these experiments. The results indicate that incorporating more drafts can further improve performance, likely thanks to higher coverage of diverse perspective of documents. Importantly, in Speculative RAG, we can launch multiple RAG drafter instances to generate drafts in parallel without additional latency.

We also examine the effect of document subset size. By varying the number of documents (1, 2, 4, or 6) sampled for draft generation on TriviaQA and PubHealth (Figure 3(b)), we find that including more documents in the context does not always lead to consistent performance improvement. While TriviaQA queries may benefit from more supporting documents due to their complexity, $\mathcal{M}_{\text{Verifier-7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ can surpass Mistral-Instruct${}_{\text{7B}}$ even with a single supporting document per draft. Furthermore, with two or more documents per draft, $\mathcal{M}_{\text{Verifier-7B}}$ + $\mathcal{M}_{\text{Drafter-7B}}$ can even surpass Mixtral-Instruct${}_{\text{8x7B}}$. This further demonstrates the effectiveness of our drafting design.

## 5 Conclusion

Our proposed Speculative RAG decomposes RAG tasks into two separate steps of drafting followed by verification.
Speculative RAG delegates the heavy lifting of drafting to a small specialized RAG drafter, while verification is done using a large generalist LM. The parallel generation of multiple drafts from diverse document subsets provides high quality answer candidates while reducing input token counts and the potential risk of position-bias-over-long-context, resulting in substantial improvements in both the quality and speed of the final output generation. We demonstrate the effectiveness of Speculative RAG with accuracy gains up to 12.97% while reducing latency by 51% compared to conventional RAG systems. Speculative RAG sheds new light on the potential of collaborative architectures for enhancing RAG performance through task decomposition.

Limitations

In this paper, we demonstrate that a smaller, specialized RAG drafter can effectively augment a larger, general-purpose LM for knowledge-intensive tasks. While Speculative RAG enhances both accuracy and efficiency, it does require training an additional drafter model. Although this step is computationally inexpensive, it adds a layer of complexity compared to using a vanilla instruction-tuned RAG model.

## Appendix

## Appendix A Prompt of Rationale Generation

Figure: Figure 4: Prompt of Rationale Generation for Gemini-Ultra

## Appendix B Prompt of RAG Drafting

Figure: Figure 5: Prompt of RAG Drafting

## Appendix C Prompt of Standard RAG

Figure: Figure 6: Prompt of Standard RAG for Non-instruction-tuned LM

Figure: Figure 7: Prompt of Standard RAG for Instruction-tuned LM

## Appendix D Case Study

Figure 8 shows two drafts generated for the same question. We observe that our RAG drafter can well understand the multiple perspectives in the retrieval results and generate high-quality drafts. Our RAG verifier can also help filter out the unreliable drafts as we observe a relatively low scores in the first draft in Figure 8.

Figure: Figure 8: Case study of Speculative RAG from TriviaQA where Dolly Parton is the corrrect answer.

## Appendix E Instruction-Tuning Settings

We construct our training dataset for the RAG drafter from diverse instruction-following pairs. We sample instances from Open-Instruct processed data (Wang et al., ) and knowledge-intensive datasets (Petroni et al., ; Stelmakh et al., ; Mihaylov et al., ). We augment the instruction-following pairs with retrieved documents and generated rationale. We use the off-the-shelf dense retriever Contriever-MS MARCO (Izacard et al., ) to retrieve up to 10 documents for each pair and use Gemini-Ultra (Team et al., ) to generate rationale. In total, we acquire a dataset of 40k instances. We use Mistral${}_{\text{7B}}$ (v0.1) as our base LM for the RAG drafter. We reproduce the performance of Self-RAG (Asai et al., ) and CRAG (Yan et al., ) with Mistral${}_{\text{7B}}$ (v0.1) for a fair comparison. We implement the training scripts using the Transformers library from Hugging Face (Wolf et al., ). We employ DeepSpeed (Rasley et al., ) to accelerate the training process. All experiments are conducted on a Linux server equipped with 16 Nvidia A100-SXM4-40GB GPUs.

## Appendix F Effects of Self-Reflection Statement

We use “Do you think the explanation supports the answers? (Yes or No)” as the self-reflection statement in our main results. In this study, we replace it with other alternatives to see how the self-reflection statement affects the accuracy. The results are reported in Table 4. We observe that the performance does not change a lot given different self-reflection statements, which shows the stable verification capability of the generalist LMs by language modeling objective.

**Table 4: Performance analysis of Speculative RAG with different self-reflection statements $R$ when computing the self-reflection score $\rho_{\text{Self-reflect}}=P(\texttt{"Yes"}|Q,\alpha,\beta,R)$, where $Q$ is the query, $\alpha,\beta$ are the generated answer draft and rationale.**
| Reflection Statement | TriviaQA | PubHealth |
| --- | --- | --- |
| Do you think the explanation supports the answers? (Yes or No) | 74.24 | 76.60 |
| Does the rationale support the answer? (Yes or No) | 74.22 | 76.09 |
| What do you think about the rationale? A good one? (Yes or No) | 74.25 | 75.79 |
| Is the rationale good enough to support the answer? (Yes or No) | 74.39 | 76.29 |

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 5
presentation: 2
questions: - For the comparison of the average number of tokens in the generated rationale and the retrieved documents in Figure ```2```: What is the number of retrieved documents? Is it before or after subsetting to drafters?

- Is the Multi-Perspective Sampling stage also considered in the latency analysis in Section ```4.5```?
soundness: 3
strengths: - The conceptual extension of the speculative farmwork proposed by the author is both novel and appealing, and supported by strong empirical results.

- The paper targets a timely challenge in RAG and offers promising techniques to improve the efficiency for the system.

- The experiments are comprehensive and thorough, in particular, the extensive ablation study and analysis provide great insights for a better understanding of the proposed approach.
summary: The paper proposed a retrieval-augmented generation framework termed SpeculativeRAG, leveraging high-level concepts analogical to speculative decoding.
The framework exhibits better performance with lower latency via lunching a set of smaller model instances in parallel, each processes a subset of retrieved documents, to produce answer drafts and rationales.
The answer drafts and rationales are subsequently verified by a larger, strong base LLM to select the final answer.
weaknesses: - The instruction-tuning of the small drafter LMs requires synthesis rationales generated by Gemini Ultra from an additional 40k instances. This presents an unfair comparison for baselines methods that do not have access to these external resources in the experiments.

- It is unclear whether multiple instances of large verifier LLMs are also lunched in parallel. According to Line ```11~14``` in Algorithm ```1```, this seems to be the case. If so, the large memory overhead might significantly offset the latency gain in practical scenarios.

- A relevant work [1] (first released ~4.5 month ago) is missing in the baseline and not mentioned in the related work Section. To the best of my understanding, [1] also proposes a very similar synthesis rationale-augmented generation approach for RAG, thus, might be a suitable method for baseline comparison.

- The contribution and result of the paper could be further strengthened if newer generation of models (e.g., gemma-2, llama-3) are adopted in the experiments.

[1] Wei et al, *InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales*. 2024.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. In Line 207-209, is the answer A obtained by human labeling or generated by LLMs given documents D? What if the D does not provide evidence for generating A?
2. Except for the inference latency, what are the consuming token numbers of different methods?
soundness: 3
strengths: 1. The paper is well-written and easy to follow. The authors present detailed descriptions of their methods, including prompts and experimental setups.
2. The proposed method achieves obvious improvements among the baselines, and the ablation studies can verify the effectiveness of their method.
summary: This work introduces a new speculative decoding method to enhance the retrieval augmented generation. Specifically, a smaller distilled specialist LM is used to generate answer drafts based on randomly selected document subsets. After that, a larger generalist LM is used to verify the answer confidences according to the answer and rationale generation probabilities.
weaknesses: 1. The conducted experiments should include more recent RAG baselines, especially the speculative decoding methods (e.g., [1], [2]). 
2. It is a trivial idea and makes limited contributions compared to previous studies. As mentioned in this paper, many recent studies investigate the two-stage RAG, where a drafting stage produces answer candidates and the assembling stage generates final answers based on the drafts. There are only a few differences between them.


[1] Ground every sentence: Improving retrieval-augmented llms with interleaved reference claim generation.

[2] RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: Since this is a paper target for the RAG framework, testing the proposed framework on some RAG benchmarks may be more reliable. Because the QA benchmark is mainly designed for reasoning, not for the RAG framework.
soundness: 3
strengths: This paper trained an LLM (Drafter) to generate multiple answers paired with rationale (reference + explains). Then, they use another LLM (Verifier)to confirm the answers, which enhances the robustness.
summary: This paper introduces Speculative RAG – a framework that leverages a larger generalist LM to efficiently verify multiple RAG drafts produced in parallel by a smaller, distilled specialist LM. Each draft is generated from a distinct subset of retrieved documents, offering diverse perspectives on the evidence while reducing input token counts per draft.
weaknesses: The main contribution of this paper is insufficient. Because the whole framework just involves clustering the documents and fine-tuning small LLM to generate answers with rationale. Then prompt Verifier model confirms the answer based on rationale.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. While the author has shown that the proposed method is effective, it is important to note that the generalist LM has not undergone fine-tuning. RAG serves to supply additional information to the LLM to mitigate knowledge gaps. However, if the generalist LM itself lacks relevant knowledge, can it reliably evaluate the rationale and drafts generated by the specialist LM, particularly when the specialist’s output is inaccurate or includes hallucinations? To better understand this limitation, could the author provide boundary cases or conduct an error analysis?
soundness: 3
strengths: 1. The designed specialist LM is lightweight and allows to analyze multiple documents and produce RAG drafts in parallel, which effectively reduces the computation cost and increases the inference efficiency.
2. The distinction between specialist and generalist LMs clarifies their functional roles, reducing the risk of generalization degradation that may result from supervised fine-tuning (SFT) of a generalist LM. Specialists, optimized through targeted fine-tuning, focus on improving draft generation capabilities, thereby enhancing result accuracy.
3. Applying a clustering algorithm, i.e. K-means, to pre-group retrieved documents and then uniformly sampling from each group, i.e., multi-perspective sampling, can mitigate analysis inaccuracies caused by incomplete information, enhancing robustness in practical applications.
summary: This paper leverages the idea of speculative decoding to design a speculative RAG framework, which offloads computational burden to a smaller, specialist LM that serves as the RAG drafter for generalist LMs. Extensive experiments and ablation studies demonstrate the effectiveness of the proposed method.
weaknesses: 1. The quality of the SFT data constructed by the author remains unclear due to insufficient analysis. Additionally, further ablation studies are needed to evaluate the drafter’s performance across varying data volumes. Acquiring substantial SFT data through a proprietary model is resource-intensive. Therefore, it is essential to investigate whether a smaller dataset could yield satisfactory results or if a large volume of SFT data is indeed required to achieve optimal outcomes.
2. P(Yes) or SR is commonly applied in hallucination detection. However, [1] highlights that LLMs without target SFT or aligned, while LLMs may differentiate between correct and incorrect results, the probability itself, i.e., P(Yes), is either uncalibrated or demonstrates poor calibration performance. Although Table 3 shows that incorporating SR enhances overall performance, there is insufficient evidence to validate the intrinsic effectiveness of P(Yes). This limitation weakens the robustness of the proposed method. Could the author also add an ablation study to independently assess the effectiveness of P(Yes)?
3. It seems that results for key baseline models, such as CRAG and Self-CRAG, are missing for the three free-form datasets. This omission is concerning, as it is standard research practice to replicate baseline results when they are not available from prior studies, especially when the authors are comparing their approach with only a few previous works. Could the authors also include the performance metrics of CRAG and Self-CRAG on the free-form datasets for a more comprehensive comparison?
4. According to the results in Table 1, Mixtral-Instruct-8x7B has already achieved high scores on TriviaQA and ARC-C (73.91% and 78.41%), and the improvement brought by Speculative RAG is limited (74.24% and 80.55%). These results may diminish the contribution of the proposed method, as the performance improvement brought by instruction tuning is more pronounced and stable (Mistral-7B vs. Mistral-Instruct-7B and Mixtral-8x7B vs. Mixtral-Instruct-8x7B). Could the authors provide more explanation and analysis to clarify this result?
5. Although the datasets used in this paper include a variety of question-answering formats, such as free-form (short-form and multi-hop) and closed-set, they are all based on wiki-style or specific domains. To more convincingly demonstrate the method's effectiveness, the authors should further extend their evaluation to more realistic and domain-diverse RAG datasets, like FreshQA and BRIGHT. Could the author also discuss the potential challenges in applying their method to these datasets and propose specific experiments to show the performance?

[1] Kadavath et al., Language Models (Mostly) Know What They Know, 2022.

Meta-review: The paper proposed Speculative RAG, a framework that decomposes retrieval-augmented generation into two stages. First, a specialist draft model produces multiple candidate responses from distinct document subsets. Then, a larger generalist LM verifies these drafts to select a final answer. The authors claim this two-step process (1) improves latency by offloading most of the generation onto a smaller model, and (2) increases accuracy by ensembling generations on diverse retrieval subsets and reducing position bias in lengthy contexts. Experimental evaluations on multiple QA benchmarks show accuracy gains and latency reduction compared to baseline RAG methods. 

Strengths
1. The idea paper of adapting speculative decoding into a RAG setting on different retrieval subsets is reasonable.
2. The proposed method achieves improvement over baseline RAG methods on multiple QA benchmarks, with noticable reductions in inference time.
3. The authors conduct thorough ablations to isolate the contributions of different components

Weaknesses
1. Multiple reviewers noted the lack of direct baselines against newly proposed speculative decoding or multi-stage RAG approaches
2. A stronger examination of more modern LLMs could strengthen the paper’s contributions.
3. Certain reviewers view the work as a relatively modest extension of prior multi-stage RAG methods.

The paper presents a novel and efficient approach to RAG, with sufficient empirical results and detailed ablations to support its claims. While some questions about novelty and evaluation were proposed by the reviewers, I believe the authors addressed most points during the rebuttal. The novelty is still a major concern, but considering its simplicity and efficacy, I recommend a weak acceptance of this paper.

Decision: Accept (Poster)