PreQRAG -- Classify and Rewrite for Enhanced RAG

Catalina Riano; Damian Martinez; Hui Fang

REVIEW 4 major objections 5 minor 1 cited by

PreQRAG -- Classify and Rewrite for Enhanced RAG

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A preprocessing layer that sorts questions by document type and rewrites them accordingly improves retrieval and answer quality in RAG systems.

desk verdict Competent challenge-system paper whose internal retrieval ablations are solid, but the central causal claim about preprocessing improving RAG is not tested end-to-end; still worth a referee's time. read the letter →

arxiv 2506.17493 v1 pith:SZATJKRM submitted 2025-06-20 cs.IR

Damian Martinez , Catalina Riano , Hui Fang This is my paper · ORCID

classification cs.IR

keywords retrieval-augmentedgenerationquestionclassificationqueryrewritingdecompositionhybridretrievalrerankingquestion-type-awarepreprocessing

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a cheap preprocessing step can make a retrieval-augmented generation system measurably better. The step classifies each incoming question as answerable from a single document or from multiple documents, then rewrites the question according to that type. Single-document questions get two retrieval-focused rewrites, one for sparse search and one for dense search; multi-document questions are decomposed into two sub-questions whose retrieved evidence is merged. The authors report that this question-type-aware strategy improves retrieval, reranking, and final answer quality, and that their system finished second in its session of the competition.

What carries the argument

The machinery is question-type-aware preprocessing: a rule-based classifier that sorts each question into single-document or multi-document, chosen because it identifies multi-document questions far more reliably than the LLM-based classifier. That label controls two rewriting branches — retrieval-optimized rewrites for single-document questions and decomposition into two sub-questions for multi-document ones — and the rewritten queries are what enter the hybrid sparse-and-dense retrieval stage. The classifier's asymmetric error tolerance, where extra context is acceptable but missing context is costly, is what makes the preprocessing scheme safe for downstream generation.

What would settle it

Run the full PreQRAG pipeline and a no-preprocessing baseline on the challenge's 500-question benchmark, scoring each answer with the same equivalence, relevance, and faithfulness rubric; if the rewritten and decomposed queries do not beat the original queries on the live set, the central claim is not supported.

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Extended reading notes

Core claim

PreQRAG's central claim is that classification and rewriting of the input question improves the whole RAG pipeline. On the paper's internal evaluations, rewriting raises Mean Reciprocal Rank for single-document questions, decomposing multi-document questions substantially raises the fraction of ground-truth documents retrieved, hybrid sparse-plus-dense retrieval followed by cross-encoder reranking produces the largest retrieval gains, and a top-three context with a role-based prompt gives the best generation results. The authors present these component-wise improvements and a second-place competition finish as evidence that question-type-aware preprocessing is an effective and inexpensive enhancement to RAG.

Load-bearing premise

The load-bearing assumption is that the synthetic questions used to tune the classifier, rewrites, and context length behave like the challenge's actual benchmark questions, so the internal gains transfer to the live setting.

Editorial extensions

If this is right

A cheap preprocessing layer can improve RAG without retraining the retriever, reranker, or generator.
Decomposing multi-document questions into two sub-questions roughly doubles the chance of retrieving a ground-truth document at Top-1 in the paper's internal evaluations.
Hybrid dense and sparse retrieval followed by cross-encoder reranking is where the largest retrieval gains appear, lifting single-document Top-1 retrieval to 64.2% in the paper's internal evaluation.
Context length choice matters: for both question types, top-three documents perform as well as or better than top-one while providing more safety when the top hit is wrong.
For a smaller instruction-tuned model, the design of prompts and context arrangement affects answer quality more than variations in the generation process.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper, the same classify-then-rewrite layer could be tested on domains whose questions do not fit the challenge's category schema, and the failure pattern would indicate how much of the gain is schema-specific.
Beyond the paper, holding retrieval and generation fixed while varying only the preprocessing branch would separate the contribution of classification accuracy from the contribution of rewriting quality.
Beyond the paper, the asymmetry in misclassification costs suggests a design rule for other RAG systems: when in doubt, treat a question as multi-document, because excess context is cheaper to tolerate than missing context.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a workmanlike challenge submission, not a research breakthrough. The novel bit is the specific combination — rule-based single/multi classification, two rewriting strategies for sparse and dense retrieval, and two-subquestion decomposition for multi-doc queries — evaluated on the LiveRAG challenge and reported as preliminary second in Session 2. That's a real result, though preliminary.

What I like: the paper actually measures things. The retrieval-level ablations are consistent and show real gains: rewriting improves MRR by ~13–14%, decomposition lifts Top-10 recall from 41% to 55% (sparse) and 36% to 56% (dense), and reranking pushes single-doc Top-10 to 85%. The design decisions are documented with experiments — generation method, decoding parameters, prompt strategies, context length. They also include a second judge (Claude) in the appendix, which is more than many papers do.

Where it's soft: the abstract's causal claim — 'this classification and rewriting strategy improves RAG performance' — is not directly tested. The internal numbers come from 216 synthetic QA pairs generated with DataMorgana using the same categorization schema that drove the system design, so those gains partly reflect tuning on the evaluation distribution. There is no end-to-end ablation on the LiveRAG questions with the preprocessing module disabled. The second-place score is a single preliminary run; it could be driven by hybrid retrieval, reranking, prompt design, or the TOP-3 context choice.

One specific issue: they say Top-1 is 'best overall' for single-doc questions in Table 4, then choose Top-3 anyway. Under equal weighting, Top-1 actually beats Top-3 (0.851 vs 0.839). The argument is robustness — better chance of hitting the gold document — which is reasonable, but it undercuts the claim that the choice follows from the numbers. Also, the faithfulness scores are judge-dependent: multi-doc Top-3 faithfulness is 0.820 with GPT-4o Mini but 0.384 with Claude. They report this, but it means the internal answer-quality metric that drives several decisions is unstable.

There's also no code or data released, which limits reproducibility — though that's typical for a challenge report.

Bottom line: the paper is honest and internally consistent, the retrieval evidence is solid, and the challenge rank gives it external grounding. The missing end-to-end ablation is the load-bearing gap. I'd send it to a referee who can ask for that ablation and a clearer treatment of the TOP-3 choice. It's a useful data point for anyone building RAG pipelines.

Referee Report

4 major / 5 minor

Summary. The paper presents PreQRAG, the UDInfo team's submission to the SIGIR 2025 LiveRAG Challenge. The system adds a question-preprocessing stage to a standard RAG pipeline: a rule-based classifier labels each question as single-document or multi-document; single-document questions are rewritten with two LLM prompts optimized for sparse and dense retrieval; multi-document questions are decomposed into two sub-questions. Retrieval is hybrid (BM25 plus E5 dense), followed by bge-reranker-v2 reranking and generation with Falcon3-10B-Instruct using a role-based prompt, a fixed TOP-3 context, and greedy decoding. The paper reports a preliminary second place in Session 2 of the LiveRAG Challenge (Table 5). Internal evaluations on DataMorgana-generated synthetic datasets show retrieval-level gains from rewriting (Table A.1), decomposition (Table A.2), and reranking (Table 3), together with generation-stage comparisons over prompts, decoding parameters, and context length.

Significance. If the central causal claim is established, the contribution is a modest, practical result: a lightweight preprocessing step that improves retrieval and answer quality in a constrained RAG setting. The paper has real strengths: the official LiveRAG ranking is independent external evidence; the retrieval-stage ablations are clearly separated by component; and the system description is sufficiently detailed to be reproduced. At the same time, the causal statement that classification and rewriting improve RAG performance is not yet fully supported, because the internal evaluations use synthetic questions generated under the same schema used for design and because the LiveRAG result is a single uncontrolled run. The modular retrieval gains are credible, but their connection to final answer quality on the official 500 questions needs an additional control or a carefully scoped claim.

major comments (4)

[§4, Table 5] The abstract's claim that "This classification and rewriting strategy improves the RAG performance" is supported only by retrieval-level experiments on 216 synthetic questions and by a single preliminary LiveRAG run with no control arm. The second-place score in Table 5 could be driven by the hybrid retrieval, the reranker, the role-based prompt, the TOP-3 context choice, or greedy decoding, instead of by question preprocessing. Please add an end-to-end ablation on the LiveRAG questions that compares the full PreQRAG pipeline with the classify-and-rewrite module disabled, or explicitly restrict the causal claim to retrieval-stage improvements.
[Appendix A.1 vs §2.3, Table 2] There is a major numerical inconsistency between the two retrieval-effectiveness tables for the same single-document rewritten sparse setting. Table A.1 reports MRR@1 = 0.033 for sparse rewritten queries, while Table 2 reports a 37.5% Top-1 retrieval rate for sparse rewritten single-document questions. These numbers cannot both describe the same configuration; they differ by an order of magnitude. Please clarify whether the datasets, the scoring definition (success at rank 1 versus reciprocal rank at rank 1), or the retrieval runs differ, because the rewriting-improvement argument rests on these measurements.
[§3.2, Appendix D] The design decisions in the paper are tuned on 110 single-document and 106 multi-document DataMorgana-generated QA pairs that use the same categorization schema as the challenge organizers. The paper does not test whether these synthetic questions are representative of the actual 500 LiveRAG questions. A concrete transfer check, such as retrieval recall with and without rewriting on the official 500 questions, would substantially strengthen the claim that the preprocessing gains transfer from the internal evaluation distribution to the LiveRAG benchmark.
[§2.5, Table 4 and Appendix C.2] The choice of TOP-3 context and other generation-stage decisions rely on GPT-4o Mini judge scores, but Appendix C.2 shows strong judge sensitivity: multi-document faithfulness at TOP-3 is 0.820 with GPT-4o Mini and 0.384 with Claude 3.5 Sonnet. This does not invalidate the pipeline, but it means the internal answer-quality comparisons are judge-dependent. Please report the judge variance explicitly or at least qualify that configuration choices are contingent on the chosen evaluator.

minor comments (5)

[§1] The introduction refers to "Falcon-3B-10B-Instruct," which appears to be a typo for "Falcon3-10B-Instruct" used elsewhere.
[Table 5 caption] The caption contains the misspelling "highligted"; it should be "highlighted."
[§2.2.2] The text states "The results in Table A.2 shows the effectiveness of query decomposition," but the comparison with the non-decomposed baseline appears only in the appendix. Consider moving at least one row of this comparison to the main body, since decomposition is a central component.
[§3.1] The faithfulness evaluation prompt in Appendix C.1 uses scores 1, 0, and -1, while the reported faithfulness values in the tables are normalized between 0 and 1. Please state the normalization formula explicitly.
[Table 1] The classification accuracy table does not report the number of questions used for each classifier. Given the small synthetic evaluation sets, adding the sample size and ideally a confidence interval would help interpret differences such as 0.731 versus 0.822 for single-document questions.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central claim rests on an external LiveRAG competition result, and internal synthetic-data evaluations are a tuning limitation rather than a definitional reduction.

full rationale

PreQRAG's headline claim—that question-type-aware classification and rewriting improves RAG performance—is grounded in the LiveRAG Challenge Session 2 result (Table 5), an external benchmark outside the authors' control. The internal evaluations (Tables A.1, A.2, 2, 3, 4, B.1-B.4) are run on synthetic datasets produced with DataMorgana using the same categorization schema used to guide design decisions. This is a genuine limitation: the rewriting prompts, rule-based classifier, and TOP-3 context choice were selected, at least in part, on data drawn from the same distribution used for the reported internal scores, so those scores are not independent confirmations. It is not, however, circular in the definitional sense. The synthetic labels and retrieval targets are generated from source documents, not from PreQRAG's outputs; no parameter is fitted to the final correctness or faithfulness metric and then reported as a prediction; and the competition result is an external observation that does not reduce to the paper's inputs. The absence of a LiveRAG ablation with the preprocessing module disabled means the causal claim is not fully established, and Appendix C.2 shows the internal answer-quality scores are judge-dependent, but these are correctness and evidence concerns, not circularity. No load-bearing derivation or equation in the paper is equivalent to its own assumptions by construction.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim rests on three domain assumptions: synthetic DataMorgana questions represent the LiveRAG distribution, GPT-4o Mini scores are a valid proxy for human quality judgments, and the original document(s) behind each question are the relevant gold standard. Two hand-picked structural choices (TOP-3 context and exactly two sub-questions) and an unspecified rule-based classifier are additional components selected on the evaluation data. No invented entities are introduced.

free parameters (3)

Context length TOP-3 = 3 documents per prompt
Generation quality depends on the number of documents in the prompt; the authors selected TOP-3 as a compromise between performance and efficiency, even though the equal-weighted average in Table 4 favors TOP-1 for single-doc questions.
Number of sub-questions per multi-document query = 2
Every multi-document question is decomposed into exactly two sub-questions (Section 2.2.2); the number is chosen by design, not derived from an optimization.
Rule-based classifier rules
The rule-based classifier that replaced Falcon after internal comparison is not described, so its rules constitute an unspecified hand-constructed component selected on the evaluation data.

assumptions (3)

domain assumption DataMorgana synthetic datasets represent the LiveRAG test distribution.
Section 3.2 and Appendix D generate the internal datasets with the same categorization schema as the challenge; all component decisions rely on this transfer.
domain assumption GPT-4o Mini scores are a valid proxy for answer quality.
Section 3.1 and Appendix C.1 use GPT-4o Mini for equivalence, relevance, and faithfulness; Appendix C.2 compares only against Claude, not against human judgments.
domain assumption The original documents behind each generated question are the relevant gold standard.
Section 2.3 states retrieval evaluation treats the document(s) used to generate each question as the only relevant documents, while acknowledging other documents may also be valid.

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Cite this review

Pith. "Pith review of PreQRAG -- Classify and Rewrite for Enhanced RAG." pith.science (2026). https://pith.science/paper/SZATJKRM

@misc{pith2026250617493,
  author       = {Pith},
  title        = {Pith review of: PreQRAG -- Classify and Rewrite for Enhanced RAG},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SZATJKRM}},
  note         = {Machine review of arXiv:2506.17493}
}

read the original abstract

This paper presents the submission of the UDInfo team to the SIGIR 2025 LiveRAG Challenge. We introduce PreQRAG, a Retrieval Augmented Generation (RAG) architecture designed to improve retrieval and generation quality through targeted question preprocessing. PreQRAG incorporates a pipeline that first classifies each input question as either single-document or multi-document type. For single-document questions, we employ question rewriting techniques to improve retrieval precision and generation relevance. For multi-document questions, we decompose complex queries into focused sub-questions that can be processed more effectively by downstream components. This classification and rewriting strategy improves the RAG performance. Experimental evaluation of the LiveRAG Challenge dataset demonstrates the effectiveness of our question-type-aware architecture, with PreQRAG achieving the preliminary second place in Session 2 of the LiveRAG challenge.

Figures

Figures reproduced from arXiv: 2506.17493 by the authors.

**Figure 1.** PreQRAG System Architecture Our proposed solution introduces a structured pipeline that begins with query preprocessing step, where each question is classified based on whether it requires information from a single document or multiple documents. Depending on this classification, the question is then rewritten to optimize retrieval effectiveness. This reformulation step is designed to improve performance for both… view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

SIGIR 2025 -- LiveRAG Challenge Report
cs.CL 2025-07 conditional novelty 3.0 of 10

In the SIGIR 2025 LiveRAG Challenge, all 25 active RAG teams beat the no-RAG baseline on LLM-judged correctness, and LLM scores correlated with human scores at r=0.88.

Reference graph

Works this paper leans on

11 extracted references · 5 canonical work pages · cited by 1 Pith paper

[1]

Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024. BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation. arXiv:2402.03216 [cs.CL]

arXiv 2024
[2]

Simone Filice, Guy Horowitz, David Carmel, Zohar Karnin, Liane Lewin-Eytan, and Yoelle Maarek. 2025. Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana. arXiv:2501.12789 [cs.CL] https://arxiv.org/abs/2501.12789

arXiv 2025
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Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024. Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997 [cs.CL] https://arxiv.org/ abs/2312.10997

arXiv 2024
[4]

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Se- bastian Riedel, and Douwe Kiela. 2021. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401 [cs.CL] https://arxiv.org/abs/ 2005.11401

arXiv 2021
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Jerry Liu. 2022. LlamaIndex. doi:10.5281/zenodo.1234

doi:10.5281/zenodo.1234 2022
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Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf. 2024. The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=n6SCkn2QaG

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Technology Innovation Institute (TII). 2024. Falcon 3B/7B/40B/180B and Falcon- Instruct Models. https://huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF. https: //huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF Accessed: 2025-05-22

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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022. Text Embeddings by Weakly-Supervised Contrastive Pre-training. arXiv preprint arXiv:2212.03533 (2022). Appendix A Questions Rewriting Results A.1 Single-document questions To assess the effectiveness of the query rewriting strategies for sing...

arXiv 2022

Show all 11 references

[9]

It includes the following categories: • Multi-aspect: Requires reasoning over two distinct aspects of a topic, sourced from two documents

Answer-Type Categorization: This schema defines what kind of information the question seeks, and whether answering it requires access to single or multiple documents. It includes the following categories: • Multi-aspect: Requires reasoning over two distinct aspects of a topic,...
[10]

• Search query: Structured as keyword-based web queries

Question Formulation Categorization: This schema classi- fies how the question is phrased: • Natural: Written in colloquial, conversational style. • Search query: Structured as keyword-based web queries
[11]

• Novice: Assumes minimal domain knowledge

User Expertise Categorization This schema captures the assumed knowledge level of the user: • Expert: Assumes familiarity with specialized terminology. • Novice: Assumes minimal domain knowledge. Table D.1 shows the datasets generated for the evaluation of the PreQRAG system. ...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024. BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation. arXiv:2402.03216 [cs.CL]

arXiv 2024

[2] [2]

Simone Filice, Guy Horowitz, David Carmel, Zohar Karnin, Liane Lewin-Eytan, and Yoelle Maarek. 2025. Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana. arXiv:2501.12789 [cs.CL] https://arxiv.org/abs/2501.12789

arXiv 2025

[3] [3]

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024. Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997 [cs.CL] https://arxiv.org/ abs/2312.10997

arXiv 2024

[4] [4]

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Se- bastian Riedel, and Douwe Kiela. 2021. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401 [cs.CL] https://arxiv.org/abs/ 2005.11401

arXiv 2021

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Jerry Liu. 2022. LlamaIndex. doi:10.5281/zenodo.1234

doi:10.5281/zenodo.1234 2022

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Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf. 2024. The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=n6SCkn2QaG

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Technology Innovation Institute (TII). 2024. Falcon 3B/7B/40B/180B and Falcon- Instruct Models. https://huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF. https: //huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF Accessed: 2025-05-22

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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022. Text Embeddings by Weakly-Supervised Contrastive Pre-training. arXiv preprint arXiv:2212.03533 (2022). Appendix A Questions Rewriting Results A.1 Single-document questions To assess the effectiveness of the query rewriting strategies for sing...

arXiv 2022