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arxiv: 2604.17325 · v1 · submitted 2026-04-19 · 💻 cs.CL

Recognition: unknown

Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation

Jiaang Li, Quan Wang, Yongdong Zhang, Yuning Wan, Zhendong Mao

Authors on Pith no claims yet

Pith reviewed 2026-05-10 06:38 UTC · model grok-4.3

classification 💻 cs.CL
keywords Retrieval-Augmented GenerationDocument RewritingQuestion-Oriented StyleLarge Language ModelsIn-Context LearningKnowledge DistillationFactual Consistency
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The pith

Rewriting retrieved documents into a question-oriented style helps LLMs use factual evidence more effectively in RAG systems.

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

The paper identifies a stylistic bias in LLMs that causes them to favor fluent generated text over factual but disorganized retrieved documents during RAG. It proposes QREAM as a rewriter that transforms documents to align with the question's style without altering facts. The method uses two stages: iterative rewriting guided by stylistic seeds, followed by distillation into a small model trained on filtered high-quality outputs. Experiments show this plug-in module raises performance in existing RAG setups by as much as 8 percent while adding almost no delay. The central insight is that how information is presented limits its usefulness more than the information itself.

Core claim

QREAM is a two-stage style-controlled rewriter: QREAM-ICL performs iterative rewriting of retrieved documents using stylistic seeds to explore question-oriented versions, while QREAM-FT distills this into a lightweight student model trained via dual-criteria rejection sampling on answer correctness and factual consistency, allowing seamless integration into RAG pipelines that improves LLM utilization of evidence.

What carries the argument

QREAM, the question-oriented document rewriter that converts retrieved passages to match query style while preserving facts through in-context exploration and filtered distillation.

If this is right

  • QREAM can be added as a plug-and-play step before LLM generation in any retrieval-augmented pipeline.
  • The dual filtering on correctness and consistency produces supervision data that maintains factual grounding.
  • Performance gains reach up to 8 percent relative improvement with negligible extra latency.
  • The approach balances question relevance against preservation of original facts in the rewritten output.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar rewriting could be applied to other input formats such as tables or code snippets to reduce presentation barriers in retrieval systems.
  • The distillation step might transfer to rewriting tasks outside RAG, such as summarization or dialogue adaptation.
  • If the stylistic bias proves general, future retrieval methods could optimize jointly for content and presentation style rather than content alone.

Load-bearing premise

The main bottleneck in RAG is stylistic mismatch that makes retrieved evidence harder for LLMs to use than generated text, and that rewriting can correct the style without introducing new factual errors or biases.

What would settle it

A controlled test on a standard RAG benchmark in which applying the rewriter produces zero or negative change in accuracy or increases the rate of factual hallucinations.

Figures

Figures reproduced from arXiv: 2604.17325 by Jiaang Li, Quan Wang, Yongdong Zhang, Yuning Wan, Zhendong Mao.

Figure 1
Figure 1. Figure 1: Illustration of different document types in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The QREAM framework operating in an Explore-then-Distill paradigm. (a-b) Stage I: QREAM-ICL explores diverse question-oriented rewrites via iterative in-context learning guided by stylistic seeds. (c) Stage II: We employ a Bidirectional Denoising mechanism to filter noisy candidates via dual-criteria rejection sampling, deriving a purified dataset to train the QREAM-FT. Iterative Rewriting as Candidate Gen… view at source ↗
Figure 3
Figure 3. Figure 3: Ablation on QREAM-ICL. The QA performance with different numbers and designs of stylistic seeds Case 1: Distracting Context (Entity Confusion) Question: what’s the dog’s name on tom and jerry? [Baseline: Compression] Doc: Tom, a grey and white domestic shorthair cat, is the main character... while Jerry is a small brown mouse... Pred: Jerry ✗ [Ours: Question-oriented Rewriting] Rewrite: Spike is a recurrin… view at source ↗
Figure 4
Figure 4. Figure 4: Performance of QREAM-ICL w.r.t. the num [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
read the original abstract

Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated context. However, LLMs often exhibit a stylistic bias when presented with mixed contexts, favoring fluent but hallucinated generated content over factually grounded yet disorganized retrieved evidence. This phenomenon reveals that the utility of retrieved information is bottlenecked by its presentation. To bridge this gap, we propose QREAM, a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts, better for LLM readers to utilize. Our framework consists of two stages: (1) QREAM-ICL, which uses stylistic seeds to guide iterative rewriting exploration; and (2) QREAM-FT, a lightweight student model distilled from denoised ICL outputs. QREAM-FT employs dual-criteria rejection sampling, filtering based on answer correctness and factual consistency to ensure high-quality supervision. QREAM seamlessly integrates into existing RAG pipelines as a plug-and-play module. Experiments demonstrate that QREAM consistently enhances advanced RAG pipelines, yielding up to 8% relative improvement with negligible latency overhead, effectively balancing question relevance with factual grounding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes QREAM, a two-stage framework for rewriting retrieved documents in RAG pipelines to adopt a question-oriented style while preserving factual content. QREAM-ICL uses in-context learning with stylistic seeds for iterative rewriting, and QREAM-FT distills a lightweight model from denoised outputs selected via dual-criteria rejection sampling based on answer correctness and factual consistency. The method is claimed to integrate seamlessly into existing RAG systems, achieving up to 8% relative performance gains with negligible additional latency.

Significance. If the empirical gains are robust and the fact preservation mechanism is reliable, this work could have practical significance for improving the effectiveness of retrieved documents in RAG by mitigating stylistic biases in LLMs. It offers a plug-and-play solution that balances relevance and grounding without substantial computational overhead.

major comments (2)
  1. Abstract: The abstract claims performance improvements of up to 8% but does not provide any details on the experimental setup, including datasets, baselines, evaluation metrics, or the specific RAG pipelines tested. This lack of information makes it impossible to verify the central empirical claim from the provided text.
  2. Abstract: The dual-criteria rejection sampling in QREAM-FT relies on LLM-based assessment for factual consistency and answer correctness. No validation of these LLM judges (such as correlation with human judgments or use of automated metrics like entailment) is mentioned, which is critical because errors in the filter could lead to corrupted training data and artifactual gains rather than true improvements from style alignment.
minor comments (2)
  1. The abstract introduces several new terms (QREAM, QREAM-ICL, QREAM-FT) without defining them upfront, which may confuse readers unfamiliar with the framework.
  2. It would be helpful to clarify what 'stylistic seeds' refers to in the description of QREAM-ICL.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, indicating planned revisions where the manuscript can be strengthened.

read point-by-point responses

  1. Referee: Abstract: The abstract claims performance improvements of up to 8% but does not provide any details on the experimental setup, including datasets, baselines, evaluation metrics, or the specific RAG pipelines tested. This lack of information makes it impossible to verify the central empirical claim from the provided text.

    Authors: We agree that the current abstract is concise and omits specific experimental details, which limits immediate verification of the reported gains. The full experimental setup—including the datasets, baselines, evaluation metrics, and RAG pipelines—is described in detail in Section 4 of the manuscript. To address this, we will revise the abstract to include a brief summary of the key experimental elements (e.g., the benchmarks used and the consistent relative improvements across pipelines) while preserving its length constraints. revision: yes

  2. Referee: Abstract: The dual-criteria rejection sampling in QREAM-FT relies on LLM-based assessment for factual consistency and answer correctness. No validation of these LLM judges (such as correlation with human judgments or use of automated metrics like entailment) is mentioned, which is critical because errors in the filter could lead to corrupted training data and artifactual gains rather than true improvements from style alignment.

    Authors: This is a fair and important point. The manuscript describes the dual-criteria rejection sampling procedure in Section 3.2 but does not include explicit validation of the LLM judges. We will add validation results in the revised version, such as agreement statistics with human annotations on sampled data and comparisons against automated metrics like textual entailment, to demonstrate the reliability of the filtering step and rule out potential artifacts. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical claims with no derivations or self-referential reductions

full rationale

The paper describes a two-stage empirical framework (QREAM-ICL for iterative rewriting and QREAM-FT for distillation via rejection sampling) and reports experimental gains on RAG pipelines. No equations, parameters fitted to target metrics, or theoretical derivations appear in the provided text. Claims rest on observed performance improvements rather than any chain that reduces to its own inputs by construction. Self-citations, if present, are not invoked to establish uniqueness theorems or load-bearing premises. The rejection sampling step is a procedural filter and does not create self-definition or fitted-input-as-prediction circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Central claim depends on the domain assumption that stylistic presentation is the key limiter in RAG utility and that fact-preserving rewriting is feasible via the described process.

axioms (1)
  • domain assumption LLMs exhibit a stylistic bias favoring fluent generated content over factually grounded but disorganized retrieved evidence
    Explicitly stated as the core motivation in the abstract.
invented entities (1)
  • QREAM no independent evidence
    purpose: Style-controlled document rewriter for RAG
    New framework introduced by the paper consisting of ICL exploration and FT distillation stages.

pith-pipeline@v0.9.0 · 5514 in / 1118 out tokens · 31343 ms · 2026-05-10T06:38:50.560258+00:00 · methodology

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