REVIEW 3 major objections 4 minor 6 references
A single perturbation-based explainer covers both retrieval and generation in RAG systems.
Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →
The paper combines earlier retriever and generator explainers into a single perturbation-based, model-agnostic framework for RAG, with brief user-study evaluations.
T0 review reviewed 2026-08-04 challenge →
load-bearing objection An honest, practical integration of retriever and generator explanations into one framework, but the evaluation is too thin and self-referential to support the full 'why' claim as stated. the 3 major comments →
Towards End-to-End Model-Agnostic Explanations for RAG Systems
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
Core claim
The paper claims that the two opaque halves of a RAG system—the retriever that selects documents and the generator that forms an answer—can be explained by one model-agnostic post-hoc pipeline. For a retrieved document, the pipeline splits it into words, removes each word in turn, re-embeds the document, and compares the perturbed cosine similarity against the untouched score; for generation, it splits the prompt into sentences, removes each sentence, re-runs the generator, and compares the new answer against the original answer. The resulting importance weights mark which terms drove retrieval and which prompt sentences drove generation. In an open-book question-answering setup, the authors
What carries the argument
The load-bearing mechanism is the leave-one-out perturbation loop: decompose the input into features, remove one feature, re-run the target component, and measure the difference. For retrieval, the features are individual words in the retrieved document and the measure is cosine similarity against the question; for generation, the features are individual sentences in the prompt and the measure is similarity between the re-generated answer and the original answer. The normalized, negated differences become feature-importance weights. The mechanism is model-agnostic because it never inspects internal weights; it only queries the retriever and generator as black boxes.
Load-bearing premise
The framework treats the drop in retrieval score or the change in answer caused by removing one word or sentence as that feature's causal importance, assuming feature effects are independent enough to measure this way; overlapping or nonlinear interactions could make a feature look important when it is not, or vice versa.
What would settle it
Construct a synthetic RAG task where ground-truth relevance is known—for example, documents where only one marked keyword determines retrieval and only one sentence contains the answer—then run the framework and compare its top-weighted words and sentences against the known decisive ones; a clear divergence would show the leave-one-out weights are not faithful. A cheaper check is to compare the framework's weights with gradient- or attention-based attributions on an open-source model, where large disagreement would show the approximated explanations do not track the model's actual use of featu
If this is right
- Any RAG system built from a black-box retriever and generator—including proprietary ones—can be explained by the same four-step perturbation pipeline.
- Retrieval explanations reduce to word-level feature weights on the retrieved document, while generation explanations reduce to sentence-level weights on the prompt.
- Because the generator-explanation weights correlate with downstream accuracy, explanation quality can serve as a practical signal for choosing among RAG models.
- Users receive a practical, approximated explanation rather than a faithful account of the model's internal reasoning; the paper argues this trade-off is acceptable for flexibility.
- The system can compose component-wise explanations, letting users compare different retrievers and generators before assembling a RAG pipeline.
Where Pith is reading between the lines
- A natural stress test the paper does not run: compare leave-one-out importance weights against model-intrinsic attributions, such as gradients or attention, on open-source retrievers and generators to quantify how close 'approximated' is to 'faithful'.
- The sentence-level generator granularity suggests a boundary: if a sentence contains both answer-bearing and irrelevant clauses, removing the whole sentence smears their contributions; finer or overlapping features would be needed for mixed-purpose contexts.
- The model-agnostic guarantee could extend beyond open-book QA to multi-hop RAG, where explanations would have to attribute across intermediate retrieval steps; the method does not rule this out, but the evaluation would be harder.
- Because explanations are validated by user agreement, a further test is whether they change user decisions—trust, acceptance, or error detection—in real RAG deployments.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes an end-to-end, model-agnostic, post-hoc explanation framework for RAG systems. The method decomposes the retrieved document and the generator prompt into features (word-level for retrieval, sentence-level for generation), perturbs each feature by leaving it out, reruns the retriever/generator, and compares the resulting score/response against the reference to derive feature importance weights. The authors report user-study evaluations from their prior work (BiTe-REx and RAG-Ex), claiming 64.7% completeness for retriever explanations and 76.9% F1 for generator explanations, and discuss the sufficiency of model-agnostic explanations despite lower user ratings than a model-intrinsic baseline. The conclusion advocates the framework as a one-fits-all solution for explaining RAG components.
Significance. If the empirical claims were fully substantiated, the framework would be a useful practical contribution: a single perturbation-based pipeline that works across dense retrievers and arbitrary LLM generators, with a released open-source interface. The paper is transparent about its limitations and explicitly notes that model-agnostic explanations are approximations. However, the manuscript as presented does not contain enough evidence to support its central causal 'why' claims, and the evaluation is deferred to self-authored prior publications. The significance is therefore conditional on either adding substantial validation or reframing the contribution as a user-intuitive explanation interface rather than a faithful account of model behavior.
major comments (3)
- [§3, Evaluation] The empirical support consists of single-point percentages and mean ratings (64.7%, 76.9%, 3.42, 3.45, 3.98, 4.04) with no sample sizes, variance, statistical tests, or baselines. The reader cannot assess whether these numbers are reliable or significant. The paper defers all methodological detail to the authors' own prior papers [2,5]; this is inadequate for a standalone contribution. Provide full evaluation details (participant counts, inter-annotator agreement, comparison conditions, statistical significance) or explicitly present the paper as a framework/system description with preliminary evidence.
- [§2 Compare; §4 Limitations] The abstract and §1 claim the framework explains 'why' the retriever retrieved a document and why the generator produced a response. But the leave-one-out importance weights are validated only against user intuition (§3), not against the models' actual decision processes. §4 concedes that model-agnostic explanations 'do not necessarily expose the working of the model by itself.' Thus the causal 'why' claim is unsupported by the presented evidence. The authors should either provide faithfulness-oriented validation (e.g., correlation with model-internal attributions, removal-based accuracy against synthetic ground truth) or explicitly reframe the contribution as user-intuitive, approximate explanations.
- [§3, Sufficiency] The conclusion 'strongly advocate[s] model-agnostic explanations' is a normative claim that goes beyond the reported data. The model-agnostic method received numerically lower user ratings (3.42/3.45) than the model-intrinsic approach (3.98/4.04), and no statistical testing is reported. The asserted 'co-relation with downstream task model performance' is mentioned without any numbers or analysis. If the sufficiency argument is to be made, it needs a quantitative comparison of trade-offs, not an unsupported advocacy statement.
minor comments (4)
- [§3 heading] Typo: 'Co-relation' should be 'Correlation'.
- [§2] The choice of word-level granularity for retrieval and sentence-level for generation is justified only by a reference to [5]; a one-sentence rationale in this paper would improve readability.
- [Figure 2] Figure 2 is never referenced in the body text and its content is not described. Add a reference and a brief caption explanation.
- [§1] The phrase 'one-fits-all' is informal; consider 'general-purpose' or 'model-agnostic across evaluated architectures.'
Circularity Check
No significant circularity; perturbation-based importance is definitional, and the self-cited evaluation uses external user annotations.
full rationale
The paper does not perform a mathematical derivation that reduces to fitted values. The core method is a leave-one-out perturbation attribution: feature importance w_i is defined as the (normalized, negated) change in retriever score or generator response when the feature is removed. This is a standard definition, not a prediction. The evaluation metrics (completeness, F1) are computed against end-user annotations, which are external human judgments rather than quantities fitted from the framework. The main methodological concern is that details of the perturbation-strategy selection and the user-study design are deferred to the authors' prior works [2,5]; however, those works report empirical user studies, which are externally falsifiable and do not assume the conclusion of the present paper. The Limitations section explicitly concedes that model-agnostic explanations are 'approximated' and do not necessarily reveal model internals, which tempers the central claim but does not make it circular. Therefore no significant circularity is found.
Axiom & Free-Parameter Ledger
axioms (4)
- domain assumption Leave-one-out perturbation produces feature importance weights that faithfully reflect the model's decision process.
- domain assumption Word-level granularity is an appropriate decomposition for explaining dense-embedding retrievers.
- domain assumption Sentence-level granularity is appropriate for explaining LLM generators.
- domain assumption User annotations of significant features are a valid ground truth for explanation quality.
Cite this review
Pith. "Pith review of Towards End-to-End Model-Agnostic Explanations for RAG Systems." pith.science (2026). https://pith.science/paper/AN2IJQH2
@misc{pith2026250907620,
author = {Pith},
title = {Pith review of: Towards End-to-End Model-Agnostic Explanations for RAG Systems},
year = {2026},
howpublished = {\url{https://pith.science/paper/AN2IJQH2}},
note = {Machine review of arXiv:2509.07620}
}
read the original abstract
Retrieval Augmented Generation (RAG) systems, despite their growing popularity for enhancing model response reliability, often struggle with trustworthiness and explainability. In this work, we present a novel, holistic, model-agnostic, post-hoc explanation framework leveraging perturbation-based techniques to explain the retrieval and generation processes in a RAG system. We propose different strategies to evaluate these explanations and discuss the sufficiency of model-agnostic explanations in RAG systems. With this work, we further aim to catalyze a collaborative effort to build reliable and explainable RAG systems.
Figures
Reference graph
Works this paper leans on
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ShailjaGupta,RajeshRanjan,andSuryaNarayanSingh.“Acomprehensivesurveyofretrieval- augmented generation (rag): Evolution, current landscape and future directions”. In:arXiv preprint arXiv:2410.12837(2024)
Pith/arXiv arXiv 2024
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[5]
RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Gen- eration
Viju Sudhi et al. “RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Gen- eration”. In:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’24. Washington DC, USA: Association for Computing Machinery, 2024, pp. 2776–2780.isbn: 9798400704314.doi: 10.1145/3626772. 3657660.url: h...
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Trustworthiness in retrieval-augmented generation systems: A survey
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Pith/arXiv arXiv 2024
This paper was first reviewed by deepseek-v4-flash on August 4, 2026.
discussion (0)
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