REVIEW 4 major objections 2 minor 28 references
Integrating attention into explanation frameworks for language and vision transformers
T0 review · 4 major / 2 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read Attention weights can be meaningfully incorporated into Shapley and concept explanations
desk verdict Two plausible attention-aware XAI methods (attention-weighted Shapley and attention-conditioned CAV derivatives), but the supplied text is corrupted so the math and experiments cannot be checked; treat it as unverified, not refuted. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The two carrying objects are (1) an attention-weighted characteristic function for Shapley decomposition — the game-theoretic payoff of a set of tokens is redefined as a sum over pairwise interactions weighted by attention, so the Shapley value assigns each token a share of the model output that respects the attention graph — and (2) attention-weighted token-level directional derivatives along concept activation vectors, where concept sensitivity is measured by how much the prediction changes when tokens are moved in the direction of a concept, with attention controlling each token's contribution. The first supplies local, per-instance attributions; the second supplies global, concept-level sensitivity scores.
What would settle it
Train a small transformer on a task with a known ground-truth token-influence rule, for example where only one token class decides the label; if the attention-weighted Shapley attributions systematically point to other tokens, or if randomizing the attention weights while freezing the value and output pathway leaves the attributions essentially unchanged, the central claim would be contradicted.
Extended reading notes
Core claim
On its own terms, the paper claims that attention weights are not merely a visualization aid but can be the building block of formal explanation quantities. For local explanations, it redefines the characteristic function used in Shapley value decomposition so that the value of a coalition of tokens is computed via pairwise interactions weighted by the attention between them; applying Shapley values to this function yields per-token attributions that reflect the attention graph. For global explanations, it defines token-level directional derivatives along concept activation vectors, with attention weights used to weight those derivatives, yielding a concept-sensitivity measure for the whole model. The empirical comparison with established methods is presented as evidence that the resulting attributions are meaningful and complementary.
Load-bearing premise
The methods assume attention weights carry trustworthy information about how much tokens influence the model's output; if attention patterns are not faithful to the computation, both the local and global explanations inherit that unfaithfulness.
Editorial extensions
If this is right
- Local explanations for a single text or image become attention-aware Shapley scores, so users can see which tokens the model's attention graph says carry the decision.
- Global explanations gain a concept-level question: how sensitive is the model to a concept, measured through attention-weighted token movements, instead of only which inputs matter.
- The same two mechanisms run on language and vision transformers, suggesting one explanation recipe across modalities.
- Compared with gradient or perturbation baselines, the attention-informed scores provide a different signal, letting explainability users cross-check conclusions.
Reading between the lines
- I infer that attention-weighted Shapley attributions could be stress-tested as a diagnostic for spurious attention: if a token receives high attribution but ablating it does not change the output, the attention graph is not faithfully reflecting computation in that case.
- An extension the authors do not pursue is using the pairwise-interaction form to recover syntactic dependencies in language transformers, since attention-weighted pairwise Shapley terms can isolate which token pairs jointly drive a prediction.
- A direct testable extension would be to compare attention-weighted concept-sensitivity scores against causal interventions on the concept direction, for example removing the concept direction from token embeddings and checking whether the sensitivity score predicts the actual output change.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes two methods for explaining transformer models by integrating attention weights into established XAI frameworks. The first method redefines the Shapley value characteristic function using attention-weighted pairwise token interactions; the second incorporates attention weights into token-level directional derivatives defined through concept activation vectors. The abstract claims these methods produce meaningful local and global explanations for both NLP and vision transformers, supported by empirical evaluations on standard benchmarks. However, the supplied full text is heavily corrupted, so the mathematical definitions, derivations, and experimental details cannot be inspected.
Significance. If the proposed methods work as claimed, the paper would contribute a novel way to leverage attention weights within game-theoretic and concept-based explanation frameworks, potentially enriching transformer explainability. The paper addresses a relevant problem and the idea is plausible. However, the central premise that attention weights faithfully reflect token influence on model outputs is not established, and the current submission provides no verifiable evidence. No code, complete derivations, or experimental tables are accessible, so the significance of the contribution cannot be assessed. The paper's potential lies mainly in its proposal, not in validated results.
major comments (4)
- [Full text (corrupted)] The body of the manuscript as supplied is unreadable due to character corruption, with no coherent equations, tables, or experimental descriptions. This prevents verification of the attention-weighted Shapley construction, the CAV derivative definitions, and the empirical claims. Since the central claims depend on these details, the manuscript cannot be evaluated in its current form.
- [Abstract] The abstract states that attention weights do not directly determine model outputs, yet the methods use attention weights as the defining input to the explanation frameworks. The paper must demonstrate that the resulting attributions reflect model behavior rather than merely re-encoding attention; for instance, by comparing attention-weighted Shapley values against causal perturbation-based attributions and showing they track output changes. Without such evidence, the claim of meaningful incorporation is unsupported.
- [Abstract] The proposed Shapley characteristic function is described only as pairwise token interactions via attention weights. The manuscript must specify the exact functional form, including how the empty coalition and full coalition are valued, and verify that the resulting Shapley values satisfy efficiency and other relevant axioms. If the characteristic function is a direct linear function of attention weights, the attributions may be deterministic transformations of attention and not genuine explanations of the output.
- [Abstract] For the concept activation vector method, the abstract mentions token-level directional derivatives but does not clarify the differentiation variable. If the derivative is taken with respect to attention weights rather than model outputs, the method would measure attention sensitivity, not concept sensitivity. This distinction is crucial for the claimed global explanation capability and must be clarified.
minor comments (2)
- [Full text] The abstract is readable, but the rest of the text is corrupted. The authors should ensure the submission is encoded correctly; this is a severe presentation issue that blocks review.
- [Introduction (as evidenced by abstract)] The paper should cite and discuss prior work on attention faithfulness, such as Jain and Wallace (2019) and related studies, to position the assumption that attention weights are informative.
Circularity Check
No concrete circular step can be exhibited; the abstract describes a constructive use of attention weights as inputs, and the full text is too corrupted to substantiate any equation-level reduction.
full rationale
The only inspectable portion of the manuscript is the abstract; the supplied full text is a corrupted extraction (it even contains an unrelated arXiv header), so no equation-level derivation chain can be quoted. On the abstract as presented, the two methods take attention weights as an input and build a Shapley characteristic function and CAV directional derivatives from them. This is a constructive design choice, not a derivation that returns its own input: the paper's conclusion that attention weights 'can be meaningfully incorporated' is an empirical claim assessed on standard benchmarks and against other explanation methods. No fitted parameter is renamed as a prediction, no self-citation is load-bearing, and no uniqueness or ansatz is imported from prior author work. The abstract's concession that attention weights 'do not directly determine model outputs' is an honest limitation and does not by itself make the method circular. The residual concern that attention-based explanations may simply re-encode attention is a faithfulness or validity question, not a circular-reasoning defect, and in any case cannot be substantiated without the missing equations and experimental details. Accordingly, no concrete circular step can be exhibited, and the appropriate finding is no significant circularity (score 0).
Assumptions & free parameters
assumptions (3)
- domain assumption Attention weights reflect patterns of token influence that can inform and complement established explanation methods.
- ad hoc to paper Redefining the Shapley characteristic function in terms of attention-weighted pairwise token interactions preserves the validity of the resulting attributions.
- ad hoc to paper Concept activation vectors combined with attention-weighted directional derivatives yield a meaningful global concept-sensitivity measure.
Cite this review
Pith. "Pith review of Integrating attention into explanation frameworks for language and vision transformers." pith.science (2026). https://pith.science/paper/IKBW27NE
@misc{pith2026250808966,
author = {Pith},
title = {Pith review of: Integrating attention into explanation frameworks for language and vision transformers},
year = {2026},
howpublished = {\url{https://pith.science/paper/IKBW27NE}},
note = {Machine review of arXiv:2508.08966}
}
read the original abstract
The attention mechanism lies at the core of the transformer architecture, providing an interpretable model-internal signal that has motivated a growing interest in attention-based model explanations. Although attention weights do not directly determine model outputs, they reflect patterns of token influence that can inform and complement established explainability techniques. This work studies the potential of utilising the information encoded in attention weights to provide meaningful model explanations by integrating them into explainable AI (XAI) frameworks that target fundamentally different aspects of model behaviour. To this end, we develop two novel explanation methods applicable to both natural language processing and computer vision tasks. The first integrates attention weights into the Shapley value decomposition by redefining the characteristic function in terms of pairwise token interactions via attention weights, thus adapting this widely used game-theoretic solution concept to provide attention-driven attributions for local explanations. The second incorporates attention weights into token-level directional derivatives defined through concept activation vectors to measure concept sensitivity for global explanations. Our empirical evaluations on standard benchmarks and in a comparison study with widely used explanation methods show that attention weights can be meaningfully incorporated into the studied XAI frameworks, highlighting their value in enriching transformer explainability.
Reference graph
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Reviewed August 15, 2026 · model on record in the stance chip above.
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