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arxiv: 2605.30790 · v1 · pith:ZYBTSGAOnew · submitted 2026-05-29 · 💻 cs.IR · cs.AI· cs.CL

On the impact of retrieved content representations in RAG Pipelines

Jonathan J Ross , Bevan Koopman , Anton van der Vegt , Guido Zuccon This is my paper

Pith reviewed 2026-06-28 21:18 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.CL
keywords RAGretrieval-augmented generationcontent representationanswer retentiongenerator accuracydocument transformationquestion answeringLLM
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The pith

Answer retention is the primary driver of generator accuracy in RAG, outweighing wording, structure, length, or query dependence.

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

The paper holds retrieval fixed and varies only the representation of retrieved documents across fourteen options, including a baseline and thirteen transformations that cover selection, summarisation, and reformulation in both query-dependent and query-independent forms. It measures question-answering accuracy from four different generators together with answer retention, defined as whether a transformed document still supports a known correct answer. The central result is that retention predicts accuracy far more strongly than any other measured property of the representation. A reader should care because this finding implies that many earlier claims about the benefits of particular transformations may rest on how well those transformations preserve answer content rather than on the transformations themselves.

Core claim

Holding retrieval fixed, varying only the representation of retrieved documents through thirteen transformations plus baseline, we observe that answer retention is the primary determinant of generator accuracy for four LLMs. When a representation maintains high retention, its specific wording, structure, length, and query dependence have limited additional effect on accuracy. This indicates that gains previously attributed to particular mechanisms may be partly due to better preservation of answer-bearing content.

What carries the argument

the answer retention metric, which checks whether a transformed document still supports a known answer and predicts generator accuracy more strongly than wording, structure, length, or query dependence

If this is right

  • Accuracy gains from specific transformations are largely explained by their impact on answer retention.
  • When retention is preserved, changes in document length, wording, or query dependence produce little further change in accuracy.
  • Prior studies attributing performance differences to representation mechanisms require controls for retention to isolate other effects.
  • Generator accuracy remains stable across selection, summarisation, and reformulation as long as the transformed document retains support for the answer.

Where Pith is reading between the lines

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

  • RAG systems could shift design priority toward retrieval and filtering steps that maximise retention rather than toward elaborate post-retrieval transformations.
  • Measuring retention on a small set of known answer pairs could serve as a cheap proxy for expected accuracy before running full generator evaluations.
  • The result may generalise to other RAG tasks such as multi-hop reasoning if those tasks also depend on the generator accessing specific retained facts.
  • Simple, query-independent representations may suffice for many cases provided they preserve answer content.

Load-bearing premise

The answer retention metric accurately captures the information actually used by the generator models.

What would settle it

Finding two representations with matching retention scores but reliably different generator accuracies, or a representation that changes retention without changing accuracy, would falsify the claim that retention is the primary determinant.

Figures

Figures reproduced from arXiv: 2605.30790 by Anton van der Vegt, Bevan Koopman, Guido Zuccon, Jonathan J Ross.

Figure 1
Figure 1. Figure 1: Overview of the experimental pipeline. KILT [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Answer accuracy versus answer retention for [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Per-query transitions between closed-book (CB) and RAG outcomes for Qwen 3.5 9B, under two RAG [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Snippet-abstractive performance as the Gemma 3 transformation model is scaled from 270M to 27B parameters. (a) Accuracy gain over closed-book versus median transformation latency for Qwen-3.5-9B; each circle denotes a transformation model size and the diamond denotes the original document baseline (no transformation). (b) LLM-judge accuracy versus transformation-model size for all four generators, with the… view at source ↗
Figure 5
Figure 5. Figure 5: Mean query-time latency per representation, decomposed into transformation latency (hatched) and [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Accuracy gain over closed-book vs. query-time latency increase over closed-book, for Qwen 3.5 9B [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Generator family bias: Does Gemma-12B favor gemma 27B transforms? "+" favors Llama-3.3-70B, "-" [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Summary transformation prompt. {section} is filled per section. Document + Query ⇒ Abstract You are creating a query-focused abstract of a document. Task: In 100 words or fewer, write a concise answer-style abstract that helps answer the question using ONLY this document. - Include ONLY information explicitly stated in the document. No outside knowledge. - You MAY paraphrase for clarity, but every claim mu… view at source ↗
Figure 9
Figure 9. Figure 9: Snippet-abstractive transformation prompt. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Paraphrase transformation prompt. {retrieved_doc} is filled per document. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Propositions transformation prompt, adapted from the proposition decomposition prompt of [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Closed-book generator prompt. {question} is filled per query. Documents + Question ⇒ Answer (RAG) Answer the question based on the provided documents. Give the shortest correct answer possible. Output only the answer. Documents: {context} Question: {query} Answer [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: RAG generator prompt. {context} is the concatenated document representations in retrieval-rank order, separated by \n\n---\n\n; {query} is the question. A.3 Judge Generated + Gold ⇒ Correct / Incorrect You are an expert evaluator for question-answering systems. Your task is to compare a generated answer to the gold (correct) answer(s) and determine if it is correct. A generated answer is CORRECT if ANY of… view at source ↗
Figure 14
Figure 14. Figure 14: LLM-judge accuracy evaluation prompt. {gold_answer} is the set of accepted answers, each on its own line as a bulleted list; {query} and {generated_answer} are filled per evaluation. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: LLM-judge answer-retention prompt, applied to the transformed gold document. [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
read the original abstract

Retrieval-Augmented Generation (RAG) supplements a language model's input with retrieved documents, yet most RAG pipelines inherit retrieval components designed for human readers. How retrieved content should be represented when the consumer is a large language model (LLM) rather than a human is less well understood. Recent work has proposed transformations of retrieved content and identified properties that affect generation, but each examines a single transformation or property in isolation, leaving open which features of a document's representation matter most. We address this with a controlled comparison: holding retrieval fixed, we vary only the representation of retrieved documents, comparing an original baseline against thirteen transformations spanning selection, summarisation, and reformulation, in query-dependent and query-independent variants. Across these fourteen representations we measure question-answering accuracy for four generators, and for each representation we also measure answer retention: whether a known answer-bearing document still supports its answer after transformation. We find that answer retention is the primary determinant of generator accuracy; notably, when retention is high, a representation's wording, structure, length, and query-dependence have limited effect. This suggests that accuracy gains attributed to specific mechanisms in prior work may be partly explained by how well those mechanisms preserve answer-bearing content, an attribution that cannot be settled without controlling for retention.

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

1 major / 1 minor

Summary. The paper claims that answer retention is the primary determinant of generator accuracy in RAG pipelines. Holding retrieval fixed, it compares an original baseline against thirteen transformations (selection, summarisation, reformulation; query-dependent and independent) across fourteen representations. It measures question-answering accuracy for four generators and answer retention (whether a known answer-bearing document still supports its answer post-transformation), concluding that when retention is high, wording, structure, length, and query-dependence have limited effect. This implies prior gains from specific mechanisms may be partly explained by retention preservation.

Significance. If the result holds, the work is significant for RAG research by offering a unifying empirical account of why different content transformations affect performance. The controlled multi-generator, multi-representation design allows isolation of retention as the dominant factor and suggests that future evaluations of representations should control for it. The direct measurement approach without fitted parameters or derivations is a methodological strength.

major comments (1)
  1. [Abstract and experimental protocol] Abstract and experimental protocol: the central claim that retention is the primary determinant requires that the retention metric closely tracks the specific content each generator actually conditions on. The abstract provides no evidence that retention (measured via an independent procedure such as string match or separate judge) was validated against generator behavior, e.g., via attention inspection or content ablation inside the generator itself. Without this, high retention could coexist with low accuracy if generators rely on different spans or inferences, undermining the attribution that other factors have limited effect when retention is high.
minor comments (1)
  1. The manuscript lacks detail on statistical tests, exact dataset sizes, precise transformation implementations, and error analysis; adding these would strengthen reproducibility without altering the core claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract and experimental protocol] Abstract and experimental protocol: the central claim that retention is the primary determinant requires that the retention metric closely tracks the specific content each generator actually conditions on. The abstract provides no evidence that retention (measured via an independent procedure such as string match or separate judge) was validated against generator behavior, e.g., via attention inspection or content ablation inside the generator itself. Without this, high retention could coexist with low accuracy if generators rely on different spans or inferences, undermining the attribution that other factors have limited effect when retention is high.

    Authors: We agree that direct validation of the retention metric against each generator's internal behavior (e.g., via attention maps or targeted ablations) would provide stronger causal evidence. Our retention procedure (Section 3.3) uses an independent judge to assess whether the transformed document still contains sufficient information to support the known answer; this is intentionally generator-agnostic to enable controlled comparison. The manuscript reports a high correlation between retention and accuracy that holds consistently across all four generators, which offers indirect support that the metric captures content relevant to generation. However, we did not perform generator-internal analyses. We will revise the abstract to briefly note the metric's design and add a limitations paragraph discussing this point, along with suggestions for future direct validation where model access permits. revision: yes

Circularity Check

0 steps flagged

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

full rationale

The paper reports controlled experiments that vary document representations while holding retrieval fixed, then directly measures generator accuracy and answer retention across 14 representations and 4 generators. The central finding is an observed correlation between retention and accuracy. No equations, fitted parameters, uniqueness theorems, or derivations are present in the provided text; the result is not obtained by construction from any input metric or prior self-citation. The retention metric is defined operationally (whether a transformed document still supports a known answer) and evaluated independently of the generator's internal behavior, but this is a validity concern rather than a circular reduction. The study is self-contained against external benchmarks and receives a normal non-finding.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work is purely empirical and relies on standard evaluation assumptions in information retrieval rather than new theoretical constructs.

axioms (2)
  • domain assumption Question-answering accuracy is a sufficient proxy for overall RAG quality
    The paper uses QA accuracy as the sole reported performance metric across generators.
  • domain assumption Answer retention can be measured independently of generator behavior
    Retention is defined and computed separately from the accuracy measurements.

pith-pipeline@v0.9.1-grok · 5766 in / 1190 out tokens · 27212 ms · 2026-06-28T21:18:43.702865+00:00 · methodology

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Reference graph

Works this paper leans on

9 extracted references · 3 canonical work pages · 1 internal anchor

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