Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability
Authors: Yarden Bakish
Itamar Zimerman
Hila Chefer
Lior Wolf
Venue: NeurIPS 2025 poster ()
Keywords: Explainability
Interpretability
Transformers
LRP
Primary area: social_and_economic_aspects_of_machine_learning
TL;DR: We show that prior LRP-based explainability methods for Transformers overlook positional encoding, and we propose a new approach that propagates relevance across positional components, yielding substantial gains in both XAI for NLP and vision tasks.

Abstract: The development of effective explainability tools for Transformers is a crucial pursuit in deep learning research. One of the most promising approaches in this domain is Layer-wise Relevance Propagation (LRP), which propagates relevance scores backward through the network to the input space by redistributing activation values based on predefined rules. However, existing LRP-based methods for Transformer explainability entirely overlook a critical component of the Transformer architecture: its positional encoding (PE), resulting in violations of conservation, and the loss of an important and unique type of relevance, which is also associated with structural and positional features. To address this limitation, we reformulate the input space for Transformer explainability as a set of position-token pairs, rather than relying solely on the standard vocabulary space. This allows us to propose specialized theoretically-grounded LRP rules designed to propagate attributions across various positional encoding methods, including Rotary, Learned, and Absolute PE. Extensive experiments with both fine-tuned classifiers and zero-shot foundation models, such as LLaMA 3, demonstrate that our method significantly outperforms the SoTA in both vision and NLP explainability tasks. Our code is provided as a supplement.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability
Yarden Bakish Tel-Aviv University
Itamar Zimerman
Hila Chefer Tel-Aviv University
Lior Wolf Tel-Aviv University
Tel-Aviv University
Abstract
The development effective explainability tools for Transformers is a crucial pursuit in deep learning research. One of the most promising approaches in this domain Layer-wise Relevance Propagation (LRP), which propagates relevance scores backward through the network to the input space by redistributing activa- tion values based on predefined rules. However, existing LRP-based methods for Transformer explainability entirely overlook critical component of the Trans- former architecture: its positional encoding (PE), resulting in violation of the conservation property, and the loss of an important and unique type of relevance, which is also associated with structural and positional features. To address this of limitation, reformulate the input space for Transformer explainability dosition-token pairs. This allows us to propose specialized theoretically-gouueee LRP rules designed to propagate attributions across various positional encoding methods, including Rotary, Learnable, and Absolute PE. Extensive experiments with both fine-tuned classifiers and zero-shot foundation models, such LLaMA 3, demonstrate that our method significantly outperforms the state-of-the-art both vision and NIP explainability tasks. Our code is publicly available.
http://gghhbb.cco/////deeeaaaaaa AWARE- LRP
Introduction
Explainable AI (XAI) is increasingly vital in deep learning (DL), where models often achieve remarkable performance but operate as opaque "black boxes" 7 16). This lack of transparency reduces trust, limits user engagement, and complicates troubleshooting, thereby restricting the use of DI. in applications where decision-making transparency is essential. Consequently, developing XAI techniques for DL models has become an important research domain [29]. This task, however, challenging, due the inherent complexity of these models, which cannot be easily represented by simple functions.
Transformer-brat based architectures, which have become dominant in DL, present additional challenges for explainability due to their large scale, often containing billions of parameters. To address this, researchers have developed various attribution methods specifically designed for Transformers [14] 2 I. Among these, model-specific XAI techniques have gained prominence, providing explanations based on the model's parameters, internal representations, and overall architecture.
The most effective model-specific XAI techniques, and the current state-of-the-art for Transformer explainability, are LRP- such [2]. LRP is well-established attribution technique that explains a model's predictions by propagating relevance scores backward through the network, redistributing activation values based on predefined propagation rules. Unlike gradient- bassee methods, which often suffer from issues like vanishing gradients or numerical instabilities, LRP provides a more stable and precise way to trace how information flows through each layer.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Recently, several refinements have been proposed to improve the stability and faithfulness of LRP rules for Transform- ers, leading to more robust and reliable interpretability techniques. Notable ex- amples include [3] and [2]. which intro- duce custom rules for propagating LRP through attention mechanisms, layer nor- malization, and other key components. Despite these advancements, we identify critical gap in this extensive line of work: all existing LRP-bbaed methods for Transformers overlook the need for PE- aware LRP rules and do not propagate attribution through positional encoding. This omission results in the loss of a key aspect of relevancy related to positional concepts, limiting the ability to provide faithful and comprehensive explanations.
Figure 1: (a) Explainability heatmaps of Attention-LRP (AttnLRP) (2). which is the state-of-the-ard LRP for trans- former explainability. (b) The LRP heatmap obtained from the positional part that is ignored by the existing LRP methods, including AttnLRP. The relevancy cap- tured by the PE component of the transformer is less sparse and captures more of the object.
To mitigate this problem, we propose Positional-Aware LRP (PA-LRP), novel
technique that significantly improves upon previous methods through two fundamental modifications: (i) Reformulating the input space of the Transformer explainability problem incorporate positional information. Instead of relying solely on the vocabulary space, we define the input space as a of position-token pairs. (ii) Introducing the first LRP rules specifically designed to propagate relevance across standard positional encoding (PE) layers, including learned PE, Rotary PE [33], and others. To enhance stability and faithfulness, our rules further improved through techniques such as reparameterization of PE layers, linearization, and defining an appropriate sink for positional relevance to ensure that position- rasition-sssocateed information properly absorbed, which we validate to be crucial for precise propagation. Moreover, we provide a complementary theoretical analysis to prove that our rules do not violate the conservation property.
We identify a critical gap in current LRP-based XAI techniques for Transformers: they overlook the attribution of positional encodings (PE). This omission results in a violation of the conservation property for input-level PE, as shown in Lemma 3.1 and leads unfaithful heatmaps when handling positional features, as demonstrated in Lemma 3.3 We empirically validate that this omission is a critical limitation by significantly outperforming existing methods, and demonstrating that in certain cases. assigning relevance to PE alone can surpass standard SoTA Transformer explainability techniques, showing that this signal is significant, as shown Tables 3 5 and Figure 3 Additionally, the obtained signal is complementary and distinct from the non- positional signal, better capturing spatial, positional, and structural relationships, as shown in Figure I (ii We introduce PA-LRP, a theoretically grounded and PE-aware technique for assigning relevance in Transformers. PA-LRP significantly outperforms previous methods across both fine- tuned classifiers and zero-shot foundation models, both NLP and vision tasks, on multiple models such LLaMA 3, DeiT, and others. (iii) Providing open-source and user-friendly implementation of our method, along with demos and practical examples, to facilitate adoption by the broader research and practitioner community.
Our main contributions consist of the following:
(i)
Background and Related Work
In this section, deseribe the scientific context for discussing LRP-based Transformer explainability, along with the necessary terminology and symbols needed to describe our method.
2.1 Positional Encoding in Transformers
Transformer-based (37 architectures rely on self-attention, which computes contextual relationships between tokens using:
QKT
Attention[X] = Softmax
(I)
ViTe

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where, K = - XWq, V= = XWv represent key, query, and value matrices respectively, dj the embedding dimension, and Wo. WK. Wy are learnable linear projection matrices.
This attention mechanism is duplicated over several "heads" and is wrapped by standard DL pe- ripherals such Layer Normalization, FFNs, and skip connections, forming the core structure of Transformer model by:
X = LayerNorm (X + Attention(X)), X" = LayerNorm (X' + FFN(X"))
(2)
where FFN applies a two-layer linear transformation with activations in the middle.
Transformers operate on sets of tokens rather than ordered sequences, making them permutation- invariant by design. Unlike architectures with built-in order sensitivity such as RNNs [21 23], Transformers require explicit positional encoding (PE) to capture sequence structure. PE can be introduced at different stages the model: can be added to token embeddings at the input layer, seen in learnable PE and sinusoidal PE (37), integrated within the attention mechanism
each layer, as employed in Rotary PE (RoPE) 33 and Alibi [26]. The key insight of this paper is that while PE is well known for its important role in the forward pass [17.. its crucial role in propagation-bar based XAI methods, such as LRP, has been largely overlooked, leading to violations of conservation and the loss of significant relevance, which often carries distinctive positional and structural meanings.
Learnable PE. Learnalle PE represents positions trainable parameters, allowing the model to learn position representations directly from data. This approach offers flexibility and adaptability.
Sinusoidal PE, introduced in the original Transformer model (37 encodes posi- tions using sine and cosine functions with different non-trainable frequencies. Because is based on absolute positions, it is less effective in tasks where relative positional information is more important.
Sinusoidal PE.
Rotary PE (RoPE). RoPE 33 incorporates positional information by rotating token embeddings in structured manner, enabling the model to naturally encode relative positions. Specifically, each key and query vector transformed using a per-position block-diagonal rotation matrix. Unlike learnable or sinusoidal PEs, RoPE encodes relative positional relationships through the multiplication of rotation matrices. Due to its effectiveness, many popular LLMs, including SAM2 (28) Pythia [12
LLaMA (36). Qwen (10), Gemma B5 and others are built top or RoPE.
Other PE techniques, such ALiBi 26 and relative PEs
31 are described in Appendix
2.2 Model-Specific XAI and LRP
Methods for explaining neural models have been extensively studied in the context of DNNs, par- ticularly in NLP 66 [39] and computer vision [30]. A widely adopted strategy for this task is the use of model-specific techniques, which exploit the internal architecture and parameters of neural models to generate explanations. One notable method this category is LRP g which propagates relevance scores, denoted by R-))) backwards through the network by redistributing activation values. Propagation relies on predefined rules and interactions between tokens.
LRP. LRP is evolution of gradient- based methods, such Input x Gradient [32 91. which often suffer from issues like numerical instabilities and gradient shattering I.. LRP enhances backpropagation rules by enforcing two key principles: (i) the conservation property, which ensures that the total relevance is preserved across layers. Namely, for a layer M, where Y = =((X), the relevance of the output R(Y) is equal to the relevance of the input R(X). (ii) The prevention of numerical instabilities during propagation. To achieve these goals, LRP rules are often derived from the Deep Taylor Decomposition principle 255. redistributing relevance scores each layer based on the first-order Taylor expansion of the layer's function.
2.3 XAI for Transformers
The first model-specific XAI methods for Transformers were based on attention maps 13 leveraging attention scores to quantify the contribution of each token to others across layers. Building on this approach, Abnar and Zuidema I introduced the attention rollout technique, which aggregates attention matrices across multiple layers to provide more holistic explanation. However, Jain and

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Wallace [22 later demonstrated that attention-based techniques can be misleading, attention scores do not always correlate with gradient-burd based feature importance measures or actual model behavior. To address these limitations, Chefer et al. [4]. developed a hybrid XAI method that combines LRP scores with attention maps, marking breakthrough in the field by improving attribution fidelity.
Purely LRP-based XAI methods for Transformers were first introduced in (38] and later refined by Ali al. [ who developed custom LRP rules tailored for LayerNorm and attention layers to preserve conservation properties and ensure numerical stability. More recently, Achtibat et al. further improved this approach by designing more faithful propagation rules for self-attention achieving saute-of-the-are art performance in Transformer explainability. To the best of our knowledge, this represents the most advanced technique in the field and serves as our primary baseline.
Interestingly, despite extensive research in this area, none of these approaches propagate relevance through PE layers. This omission leads loss of significant relevance associated with positional and structural features, ultimately resulting in less faithful and holistic attributions.
3 Method
Propagation Of Cositionl-Assoociate Relevancy Over Attention and Input level PE
In this section, we describe our PE-aware LRP rules. We first revise the input space used in the Transformer explainability prob- lem in Section 3.1 Then, building upon this formulation, we define our custom LRP rules in Section and [3.3] Finally, in Sec- tion 3.4 we provide theoretical analysis of our method, showing that our PE-aware LRP rules are theoretically grounded.
Reformalated Embedding Space (ø 1), (22, . (#t, L)
Semantioal Encoding X1, Xz,
Positional Encoding 1, 2.
Rotation Matrices R₃, R2. R₁
3.1 Reformulating the Vocabulary Space
Transformer Block
RyQs
R,K,
To comprehensively attribute positional infor- mation, we must define a sink that absorbs position-assoociteed relevance. To achieve this, we reformulate the explainability prob- lem for Transformers. Given an embedding matrix E E RIVIXD where [V] is the num- ber of tokens in the vocabulary V, and D is the embedding size, previous methods tradi- tionally define the input space S as
Rotary Attention
v4
:
Transtomer Block
Rotary Attention
S= (E, E, ERR),
(3) Figure 2: Visualization of our method for propagat- ing PE-associated relevance. Blue arrows indicate the forward path, while purple arrows represent the LRP propagation rules. Dashed arrows denote custom position-aware rules defined in our method.
where E, is the i-th row of the embedding matrix E.
In contrast, we reformulate the input space in the following manner:
(4) where is the sequence length, D' is the dimension of the positional embeddings, and K is the number of layers. Thus, in our formulation, each token in the input space consists of two ingredients: one representing the per-layer positional embedding Pa, for all layers k E [K]. and the other representing the semantic embedding E..
S = {EE,, Py.. Pj.. Py.)) E J
E
[3], ER
We define a separate sink for positional relevance at each attention layer to ensure that the omission of certain positional features in one layer does not obscure override essential features and relevance captured in other layers, and to validate that important positional attributions are not discarded.
Building upon the formulation of Eq. 4 the next two sections define LRP rules that enable stable propagation of relevance from standard positional encoding techniques to j [T.] Section 3.2 discusses input-level PE, while Section 3.3 covers attention-level PE.

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3.2 LRP-rules for Input Level PE
We begin with the simplest form of positional encoding learnable PE and then demonstrate that other input-level PEs can be reparameterized in a similar manner. For brevity, we assume that P, is vector rather than a matrix, namely P3 = P31.- We also the embedding dimensions of both the semantical and positional vectors (T) = D').
encoding-leammbll
Learnable PE. This layer learns positional information during training through an embedding
matrix P E RLXD where D represents the embedding dimension of positional information, and L denotes the maximum sequence length. For each sample, the positional and semantic embeddings are summed to obtain the final input representation. Formally, the combined embedding for the token at position j with token index is given by PJ + E, Thus, we can propagate relevance from the input of the first transformer block R 21), to the positional component P, of token by using the standard LRP epsilon rule for addition
R(2,) PA-LRP for input-level PE: R(P,) = P, P3 + E
(5)
Sinusoidal PE. This method encodes position information via unique vector of sine and cosine values constructed by:
Sinusoidal 2E()22 = sin 10000
Sinusoidal PE() + 1] cos
(6)
10000 ?
Thus, the values derived from Eq. 6 can be used to reparameterize the embedding matrix E, replacing the learned vectors with their corresponding sine and cosine values. Such reparameterizatioo elimi- nates the need to propagate gradients through non-linear functions such as sine and cosine, improving efficiency and stability.
3.3 LRP-rules for Attention-level PE
For attention-level PE, we focus on describing the PA-LRP rules for RoPE [33] as a representative example. For the derivation of the PA-LRP rules for ALiBi (26), we refer the reader to Appendix B At each layer k, RoPE modifies the queries (Q) and keys (K) matrices before computing the attention scores. This modification is done by multiplying each key and query vector by position-depentee
rotation matrix Ry,k E IR DxD where j E [L]. The rotation matrix is a block-diagonal matrix defined follows:
cos (())
sin ((1)
sin 1)
cos
0
0
Vj E [L],k E [K] Rz
cos (12))) 2
0
sin (D/2)
sin 0
( cos 0 (/2))
where each rotation angle 0/m) is defined as m(m) = jum. where wi 10000 1 Siaa
Note that in RoPE, as in other attention-level positional encodings, the positional information is represented by a matrix R Accordingly, we assume: Py, = Flattening(RR, (R,,x. D' = D². Thus, we can propagate relevance from R(R,,s) to R(P,,) by unflattening the relevance.
Now, key remaining step is define how relevance should be propagated Ry. The RoPE computation is executed before computing the attention scores, transforming the per-position queries and keys are follows:
QKK"
Yj E [ : Q, = RR K, RR,K, Rotary Attention Softmax
V.
(8)
Vdh
Our formulation builds on top of AttnLRP 2, which propagates relevance over the queries Q and keys K, resulting in their corresponding relevance scores cores ((K)). To propagate relevance
5

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from these matrices to the rotation matrices R,. we apply the standard uniform-LRP rule for matrix multiplication separately each key and query, then summing both terms to produce final attribution map per- attention layer, as follows:
Vj E [ ((R)) - | 1 +
(9)
Up to this point, we have described the PA-LRP rules for a single attention layer. However, transformer-based models stack M transformer blocks. Thus, we interpret the positional infor- mation as a vector that is passed from the input to all attention blocks via a semi-skip connection mechanism, illustrated in Figure 2 across PE layers, some of the relevance is lost, leading to unfaithful explanations that ignore position- associated aspects. Consequently, positional relevance from all layers is aggregated according the LRP addition rule, similar to skip connections.
This interpretation explains why without propagating relevance
Overall Method. Our PA-LRP rules allow us to assign relevance to the positional part of the input space. For the non-positional part, we use the same rules as defined AttLLP (2]. Finally, aggregate the relevance scores by summing their corresponding absolute values across feature dimensions, similar to previously proposed methods. built on top of the AttnLRP framework, they are not limited to it. Our input-level PE rules can be decoupled and applied to any LRP method, while the attention- PE rules can be integrated with altermative formulations, as long as they propagate relevance through the attention matrices and preserve the connection between PE and the computational graph.
worth noting that although our rules are
As a result, similar to other LRP methods, our approach can produce explainability maps with computational efficiency comparable to single backward pass. We further clarify that although our method introduces several modifications in the forward path and input space, it does not require any changes to the transformer itself. Instead, these modifications propose an equivalent forward path that allows us to better define the propagation rules.
3.4 Theoretical Analysis
To support our PA-LRP rules, we now provide theoretical evidence demonstrating that they satisfy the key LRP criteria. First, the following two lemmas prove that our proposed LRP rules satisfy the conservation property.
Lemma 3.1. For input-level PE transformers, the conservation property is violated when disregarding the positional embeddings' relevancy scores.
Lemma 3.2. For attention-level PE transformers, our PE-LRP rules satisfy the conservation property.
Next, we present a lemma based on a key example illustrating that existing methods exhibit low faithfulness. In particular, show that within simplified settings, IRP yields unfaithful explanations when the task relies heavily on positional features, such as predicting the number of tokens.
Lemma 3.3. For attention-level PE transformers, current LRP attribution rules achieve low faithful- ness, especially when considering positional features.
The proofs and examples detailed in Appendix E
4 Experiments
To assess the effectiveness of our PA-LRP rules, we perform a comprehensive set of experiments across both Vision and NLP domains. First, in Section 4.1 we conduct experiments in the Vision domain using DeiT, including perturbation and segmentation tests. Next, Section 4.2 we perform perturbation tests, ablation study, and conservation analysis in NLP. Test results are reported in each subsection, whereas the complete statistical analysis, including variance measures and paired t-test scores, is provided in Appendix
We begin by describing our baselines, ablation variant, and evaluation metrics:
Baseline and Ablation Variant. Our primary baseline for comparison is AunLRP 2), as represents the To in general transformer XAI, and our method builds on top of it for non-positional

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components, The key distinction between our approach and this baseline (as well as other LRP-based methods) is our ability attribute relevance to positional information. Our composite approach that balances both positional and non-positional relevance is denoted as PA-LRP, 'ours". Additionally, isolate the effect the positional encoding, we introduce an ablation variant denoted by 'PE Only', which directly measures the relevance assigned to positional components at the input space using our custom attribution rules.
Although empirical evaluation of attribution methods is inherently challenging, we validate our PA-LRP method using perturbation and segmentation tests. Below, we describe these metries:
Perturbation Tests. Perturbation tests are split into two metrics: positive and negative perturbations, which differ in the order in which pixels or tokens masked. In positive perturbation, pixels or tokens are masked in descending order of relevance. An effective explanation method identifies the most influential regions, leading to noticeable drop in the model's score (measured in comparison to the predicted or target class) as these critical areas are gradually removed. In negative perturbation, masking begins with the least relevant elements and progresses toward more important ones. A reliable explanation should keep the model's 's prediction stable, demonstrating robustness even when unimportant components are masked.
Following 3 in both Vision and im- age domains, the final metric is quantified us- ing the Area-Under Under-Curve (AUC), capturing model accuracy relative to the percentage of masked pixels or tokens, from 10% to 90%. For further technical details, Appendix H
Segmentation Tests. For attribution meth- ods in vision, segmentation tests are set of evaluations used to assess the quality of a model's 's ability to distinguish foreground from background in an image.
These tests compare the labeled segmentation image, which indicates whether each pixel belongs to the background or the foreground, with the explainability map after it has been bi- narized using a thresholding technique. Then, several metrics are computed over both im- ages: (i) Pixel Accuracy: The percentage of correctly classified pixels, measuring how well the predicted segmentation aligns with the ground truth. (ii) Mean Intersection-over Union (mloU): The ratio of the intersection to the union of the predicted and ground-truth segmentation maps, averaged across all im- ages. (iii) Mean Average Precision (mAP): A metric that considers precision and recall trade-offs at different thresholds, providing a robust assessment of segmentation quality.
4.1 Results for Vision Transformers
For vision models, we present both quantita- tive and qualitative analysis.
Qualitative Analysis. For qualitative anal- ysis, we visualize the explainability maps ob- tained from our method, the AttnLRP [2] base- line, and the ablation variant that focuses ex- clusively on PF-associated relevance denoted by Only. Additional examples with larger images are presented in appendix C
(b)
(c)
(d)
Figure 3: Results of different explanation methods
for DeiT. (a) Input image. (b) PA-LRP (ours), which
includes PE attribution. (c) PE only LRP, (d) At-
uLRP 2 which does not attribute relevancy to PE.

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Figure 3 presents a comparative visualization of these maps. The results reveal three notable trends. (i) Effectiveness of PE-associated relevance: The maps from the ablation variant perform the same level as the AttnRR RP haseline. This finding highlights the strength of our method in identifying important signals that previous works have overlooked, underscoring the importance our PA-LRP rules. (ii) The uniqueness of PE-associated relevance: The attributed signal derived solely from ceational-associate relevance captures unique relationships, exhibiting clearer spatial and structural patterns. In particular, relevance is distributed across the entire object, especially in the snake, bird, and shark examples. contrast, the baseline method, which does not propagate relevance through PEs, produces sparser pattern that does not focus the entire object but instead is highly selective to specifie patches. One possible explanation that captures concepts related to position, structure, order, and broader regions within the image. (iii) The importance balancing: It is evident that the maps obtained from the PF-associated method and the baseline complementary, and their combination, extracted via approach, provides the most robust explanations.
relevance better
Quantitative Analysis. Here, we present our quantitative results through perturbation and segmen- tation tests.
Perturbation Tests in Vision. The results for perturbation tests are shown in Table where compare our method against the attention LRP baseline. Experiments are conducted using three model sizes: Tiny, Small, and Base.
Notably, our method outperforms the baseline by a significant margin. For instance, in negative perturbation of the predicted class, method improves the performance by average of 3.97 points across the three model sizes. However, in positive perturbation, our method lags behind the baseline in half of the cases, though by a small margin of at most 1.2 points.
Table Perturbation Tests for DeiT Variants on ImageNet, AUC results for predicted class. Higher (lower) is better for negative (positive).
M. Size
Method
Negative f
Positive + Predicted Target
Predicted
Target
Base
AttnLRP
52.185 54.970
47.516
10.784
8.032 9.237
Base
Ours
50.174
9.918
Small
AttnLRP
50.662
45.105
10.511
9.761
Small
Ours
53.482
47.948
9.135
8.477
Tiny
AttnLRP
43.832
37.499
2.796
2.503
Tiny
Ours
50.1241
42.567
3.579
3.214
Segmentation Tests in Vision. As for segmentation tests, the empirical analysis in Tab. 2 clearly demonstrates that our method outperform the AttnLRP [ baseline. In particular, our method improves over the baseline by 1 1% points Pixel Accuracy, and 2% points in mloU (Mean Intersection over Union). These results further highlight the importance of positionally associated relevance in effectively capturing spatial relationships and representing entire objects more accurately.
Table 2: Segmentation performance of DeiT variants on ImageNet segmentation 20 better.
Higher is
M. Size
Method
Pixel Acc.
mIoU t
Base
AttnLRP
72.204
50.100
Base
Ours
72,698
51.400
Small
AttnLRP
72.114
50,000
Small
Ours
73.060
51.700
Tiny
AttnLRP
74.815
52.850
Tiny
Ours
76.613
55.920

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In our experiments, we followed the same guidelines reported as optimal in AttnLRP 22. specifically. a combination of the e-rule and the y-rule. We report additional quantitative results in Appendix J extending our method to the a-8 propagation rule.
4.2 Results in NLP
For experiments in the NLP domain, we first present results for perturbation tests, including an ablation study, followed by an assessment of the conservation property. For our tests, we adopt
settings defined in 4. To demonstrate the general superiority of our method beyond LRP-based
approaches, we extend our evaluation to additional XAI techniques: Integrated Gradients (IG) [34] and Slalom (24) for classification tasks, and IG [34 and SHAP [18] for zero-shot settings. We note
that SHAP is significantly more computationally involved, allowing us to evaluate this method only
for smaller context-sizes inputs. We present qualitative results in Appendix, D
Table 3: Perturbation Tests NLP. Evaluation of LLaMa-2 7B and Tiny-LLaMa, finetuned on IMDB, on pruning and generation perturbation tasks. AttnLRP [2] is the LRP baseline. The metrics used are AUAC (area under activation curve, higher is better) and AU-MSE (area under the MSE, lower is better).
Model
Method
Generation
Pruning
AUAC 1
AU-MSE
AUAC f
AU-MSE
+
LLaMa-2 7B
IG
0.556
24.473
0.556
24.438
LLaMa-2 7B
Slalom
0.606
18.375
0.636
7.315
LLaMa-2 7B
AttaLRP 0.779
7.629
0.777
6.548
LLaMa-2 7B
PE Only 0.771
6.792
0.771
6.823
LLaMa-2 7B
Ours 0.796
6.521
0.790
6.325
Tiny-LLaMa-2 7B
IG
0.637
13.745
0.636
13.770
Tiny-LLaMa-2 7B
Slalom 0.611
15.408
0.608
15.666
Tiny-LLaMa-2 7B
AttnLRP
0.803
8.065
0.792
4.030
Tiny-LLaMa-2 7B
PE Only 0.788
3,918
0.788
3.947
Tiny-LLaMa-2 7B
Ours
0.806
4.915
0.805
4.082
Perturbation Tests for Finetuned Models. We conduct perturbation tests on two LLMs, finetuned on the IMDB classification dataset: LLaMa 2-7B (36), and -ina-LLaMM 40). The results presented in debbonddemosttrrtee that method achieves better scores than the LRP baseline across all metrics and models. In particular, our approach improves the AU-MSE score in the generation scenario by 14.5% for LLLMM 2-7B and 51.41% % for Tiny-LLaMa. To examine the effect of quantization on attributions, we provide additional results for a quantized version of LLaMa 2-7B in Appendix [K]
19 to evaluate explainability performance zero-shot setting. The results presented in Table showcase the superiority of our method across all metrics. (i) Multiple-Choice Question Answering (MCQA): our approach improves, on both generation and pruning scenarios, the AUAC score by approximately 3.2%, and AU-MSE score by approximately by 7.7%. (ii) Next Token Prediction: our approach improves the AUAC score by approximately 0.5% on both generation and pruning scenarios, and AU-MSE score by approximately by 3% both scenarios. In contrast to MCQA, the Wikipedia dataset consists relatively long texts, making shifts in relevancy distributions less critical to the model's prediction.
Perturbation Tests in Zero-Shot Settings We use LLaMa 3-8B
We conduct perturbation tests for the method that attributes solely positional-associtedd relevance. The results are presented in the second, fourth, and sixth rows of Table S and second row of 5] Surprisingly, this method produces results similar to the AttLRP baseline, demonstrating the importance of PE-associated relevance, which carries a significant part of the signal. In particular, this variant achieves the best score on the AU-MSE metric for Tiny- -LLMM,, reducing the error by 50% compared to AttnLRP 2 Moreover, in Table we ablate the contribution of our multi-sink approach, which is designed to prevent the loss of positional relevance. We evaluate explainability performance for binary classification of I.I -aM----BB, using the same perturbation metrics, and report that the multi-sink approach improves the results by 7%.
Ablation.

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Table Ablation Study:
Analyzing the contribution the multi-sink mechanism via perturbation tests in NLP. The evaluation was conducted on LLaMa-2-7B using the IMDB dataset.
Method
Generation
Pruning
AUAC
AU-MSE + AUAC f AU-MSE
Ours
0.796
6,521
0.790 0.758
6.325
w/o Multi-Sink
0.759
7.124
7.158
Table 5: Perturbation Tests in NLP (Zero-Shot). Evaluation of LLaMa-3 8B in zero-shot on generation and pruning perturbation tasks for both multiple-choice question answering and Next- Token Prediction (NTP) settings. Metries reported are AUAC (area under activation curve, higher is better) and AU-MSE (area under MSE, lower is better). "AttnLRP" refers to the LRP baseline [2]. 'G' for generation and "p" for pruning.
Multiple Choice Question Answering
Next Token Prediction
Method
G. ALAC 1
G. AU-MSE 1 AUAC
P. AU--SSE
G. AUAC
AU-MSS + P. AUAC
P. AU-MSE
SHAP
0.291
141.757
0.282
147.229
0.351
119.984
0.339
123.212
0.481
40.681
0.481
40.750
AttaLRP
0.365
66.399
0.354
68.856
0.559
41.704
0.559
42,003
PE Only
0.374
61.014
0.364
63.141
0.557
40.538
0.556
40.800
Ours
0.377
61.285
0.368
63.424
0.562
40.474
0.561
40.735
Discussion: The Role of Attributing PEs
Our theoretical and empirical analysis suggests that both semantic and positional relevance are complementary, and combining them is essential to provide precise explanations. LRP-type attribution creates pixel-level heatmaps, but can characterize and identify which concepts are attributed mainly by positional relevance versus semantic relevance?
We may expect, for example, that objects that usually placed in specific contexts (boats water, airplanes in the sky) would display a more significant PE component. Much of this position context relative. RoPE, for example, captures relative position through the matrix multiplication of two position-dependent rotation matrices, which plays fundamental role in capturing spatial features in vision tasks (e.g., objects spanning across multiple patches) and when modeling relationships between words in the same sentence in NLP. In such cases, our PA-LRP rules can effectively attribute positional features, that are largely ignored by standard LRP methods.
Conclusions
This paper explores the importance of assigning LRP scores to positional information, crucial component of Transformers and L.LMs. Our theoretical and empirical analysis demonstrates that caatiionllaasociatee relevance carries unique type of significance and can drastically improve XAI methods for attention models.
Regarding limitations, we emphasize that our work focuses on designing new custom LRP rules to propagate relevance through PEs, leveraging the insight that this aspect has been previously overlooked. However, we do not extend this insight to redesign or systematically revisit existing LRP rules. Such redesign could offer an opportunity to empirically and theoretically establish improved LRP rules for attention mechanisms and Transformer models.
7 Acknowledgments
This work was supported by a grant from the Tel Aviv University Center for AI and Data Science (TAD). This research also supported by the Ministry of Innovation, Science & Technology .Israel (1001576154) and the Michael J. Fox Foundation (MJFFF022407).
10

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A Background for Additional PEs
In this appendix, we introduce additional PEs beyond those presented in Section
Relative Positional Bias (RPB). Similar to Alibi, RPB [27] modifies the attention scores by introducing learnable bias term that depends the relative distance between query and key tokens. For query at position i and key at position j. the attention scores are adjusted as follows:
A, A + B(l -j)
(10)
where Bi j) is learned bias function that depends only the relative position difference j). rather than the absolute positions.
Attention with Linear Biases (ALiBi). ALiBi [26] is positional encoding method designed to help transformers generalize to longer sequences when trained on shorter ones. Instead of using explicit positional embeddings, ALiBi modifies attention scores directly by introducing a learned linear bias that penalizes attention weights based on token distance.
Specifically, for query token at position j. Alibi adjusts the attention scores as follows:
A'o = A. + m(li - -jl)
(11)
where m is learned or predefined slope that controls how quickly attention strength decays with distance. Different attention heads can use different slopes, enabling some heads to focus more on local interactions while others capture long-range dependencies.
B PA-LRP Rules for Alibi
Recall the main modification in the ALiBi computation:
Al.3 = A., Pt.j, where P., = =[i -il
(12)
Adopting the same approach presented for RoPE, given the relevancy scores of A denoted by R((gg)) we define specialized rules to propagate relevancy from A', to the positional terms of ALiBi at each layer, namely, indices and j. We begin by distributing the relevancy scores between Aig and P.j. using the standard e-rule for addition, giving us:
R(A(.)
R(Pag) P A PaJ +E
(13)
We proceed to propagate the relevancy scores R(P..s) ) to the positional encoding and in similar fashion to our rules for RoPE. We make the following observations: (i) m is a constant, resulting in 100% of the relevancy to propagate from P., to j- (ii) Since are using auto-regressive models, we get that > j. allowing us to ignore the absolute value function (iii) The standard c-rule for addition applies the same subtraction, as we can express - j and since -1 is constant, we propagate the entire relevancy to j. That gives us:
(-j), and also -j (-1) j.
R(Pr.)) R(i) i i-))+++
R(P.,)
R(j) R( R(- = (-))+
(14)
From hereon we adhere to our PA-LRP rules, aggregating the relevance scores of the positional terms across all layers as employed in Section
C Visualizations Images
we provide more examples in Figures 4 6 As previously explained, PE- associated relevance better highlights the entire object, and overcomes the issue of over-consideration of the foreground, where extremely high relevancy scores are produced for patches which are more concerned with semantics or common patterns, like a bird's beak in the first row in Figure
In addition to Figure 3
14

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Figure 4: Additional Qualitative Results In Vision. Results of different explanation methods for DeiT. (a) The input image. (b) PA-LRP (ours), which include PE relevancy attribution. (c) PE only LRP, (d) AttLRR 2), which does not attribute relevancy to PE.
15

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Figure 5: Additional Qualitative Results In Vision. Results of different explanation methods for DeiT. (a) The input image. (b) PA-LRP (ours), which include PE relevancy attribution. (c) PE only LRP, (d) AttLRR 2), which does not attribute relevancy to PE.
16

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GOOD
GOOD
GOOD
GOOD
Figure 6: Additional Qualitative Results In Vision. Results of different explanation methods for DeiT. (a) The input image. (b) PA-LRP (ours), which include PE relevancy attribution. (c) PE only LRP, (d) AttLLRP 2), which does not attribute relevancy to PE.
17

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D Visualizations - Text
We present qualitative results for NLP in Figure 7 can be seen that our method demonstrates better results in highlighting tokens crucial for prediction, along with their surrounding context, emphasizing it's superiority draw relevancy based on both semantics and positionally. In (b) see that the amount of artifacts is reduced drastically, with more relevancy channeled to the tokens essential for prediction ("They should have been giving a tribute to Branagh for bringing us one of the greatest films of all time").
18

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Great just great The West Coust got ty " Hurry Cal la han, the East Coast got Sh urk y. urt Reyn olds plays Sh ark y in Sh ark y s Machine and enjoyed every minute of it, Play ing a ver ick are ics cop Atlanta. G A just what everyone wants Instead of susp ension. he is sent to vice squad Like in the D irty Hurry mov ies or any other cop mov ies the captuin is always going to be the j erk, When was a kid, I was curious what
movie meant" Sh ark y *Machine" Well knew who played Sh ark y. wonder what his machine was was his GROUP of fellow ops . After cover ing the murder, be goes all out to find the per p. When it turns out to be a big time mob b oss Sh ark y doesa I play around. When be gets the other prost itute into safety. Sh ark y I fights back hard and good despite losing a finger to the ug. And I also like the purt where the bad gets blow
of the building through plate glass window That was the B OM B R andy C raw ford x" St reet Life really pat the movie in the right m ood, and the movie itself is really a great hit to me. AL WA YS R ating 4 out of 5 stars
Soa Great just grest The West Coast got" Dir Harry Cal la han, the East Coast got Sh ark y B urt Reyn olds plays Sh ark y in Sh ark y s Machine" and I enjoyed every minute of it. Play ing a ma ver ick are ot i sent to vice squad. Like in
ics cop in Atlanta, G A is just what everyone wants Insteud of susp ension. the D irty Harry mov ies or any other cop mov ies the captain always going to he the j erk, When was kid. I was curious what that movic meant" Sh ark y s &Machine" ". Well knew who played Sh ark y I wonder what his machine was was his GROUP of fellow c ops . After un cover ing the murder. he goes all out to find the per p. When it other prost itute into sufety. Sh urk fiights back hard and good despite losing finger to the th ug And I also like the part where the bad gets blow n out of the building through a plate glass window That was the B OM B ! R andy raw ford " St Life really put the movic the right m ood, and the movie itself really great hit me. AL WA YS R pting 4 out of 5 stars
out be big time mob oss Sh ark y doesn It play around. When he gets the
Esc Great just great The West Coast got Dir ty Harry Cal la han. the East Coast got Sh ark y urt Reyn olds plays Sh ark y Sh ark y 's Muchine und I enjoyed every minute of it Play ing a ver ick ies cop in Atlanta, G A is just what everyone wants. Instead of susp ension. he" 's sent to vice squad. Like in the D irty Harry mov ies or any other cop mov ies , the captain is always going to be the j erk. When was kid, I was curious what that movie meant" Sh ark y Machinee Well knew who played Sh ark y wonder what his machine was It was his GROUP of fellow ops After un cover ing the murder, he goes all out to find the per p. When it tums out to be a big time mob oss Sh ark y doesn" I play around. When he gets the other prost itute into safety. Sh y figght back hard and good despite losing a finger
ug- And I also like the part where the bad gets blow out of the building through plate glass window That was the B OM B ! R andy C raw ford s St reet Life really put the movie in the right m ood, and the movie itself is really a great hit to me, AL WA YS! R ating 4 out of 5 stars
I went to see Ham let because I was in between jobs I figured 4 hours would be greut I ve been a of Bran agh: ; Dead Again. , Henry V. was completely over wh el med by the direction acting. cinemat ography that this film captured, Like other reviews the 4 hours passes swift ly Bran agh doesn play Ham let he is Ham let. he was born for this When I watch this film I m constantly trying to find fault s. I ve go s and haven 't noticed them. How was able move the camera with all the mirror is mystery to me. This movie was shot in 7 0 mil It shame that Columbia hasa released a W ides creen version of this on V HS I own a DVD player. and I d take this over T itan ic any day. So Columbia you' to listening put this film out the way should be watched g And I don' It know what happened at the O sc ars This should have swe pl Best Picture. Best ctor, Best D irection best cinemat ography What films were they watching ! I felt sorry for Bran agh at the O sc ars when he did a I ribute to Shakespeare on the screen They should have been giving a ribute to Bran agh for bringing us one of the greatest films of time|
looked
the Hall
I went to see Ham let because I was in between jobs I figured 4 hours would be great I ve been a fan of Bran agh: Dead Again, , Henry V. I was completely over wh el med by the direction acting cinemat ography that this film captured| Like other reviews the hours passes swift ly Bran agh doesn 'I play Ham let he is Ham let. he was born for this When I watch this film I m constantly trying to find fault s. I ve looked at the go of s and haven 't noticed them How was able to move the camera in and out of the Hall mystery to me. This movie was shot in 7 0 mil It a shame that Columbia hasn released W ides creen version of this on V HS I own DVD player. and I d take this over T itan ic any day So Columbia you' re listening put this film out the way should be watched g And I don't know what happened at the se ars This should have swe pt Best Picture. Best ctor Best D irection best cinemat ography What films were they watching ? I felt sorry for Bran agh at the O sc ars when he did t ribute to Shakespeare on the screen They should have heen giving a ribute to Bran agh for hringing us one of the greatest films of all time
with all the mirror
I went to see Ham let because I was in between jobs I figured 4 hours would be great I ve been a Fan of Bran agh: Dead Again, , Henry V. I was completely over wh el med by the direction, acting. cinernat ography that this film captured, Like other reviews
hours passes swift ly . Bran agh doesn 't play Ham let he is Ham let, he was born for this When I watch this film I ' constantly trying to find fault s. I ve looked at the go of s and haven 't noticed them. How he was able to move the cumera in and out of the Hall mystery to me. This movie was
with all the mirror released W ides creen version of this on V HS I own DVD player and I d take this over T itan ic any day So Columbiu if you' re listening put this film out the way should be watched | And I don know what happened at the O se ars. This should have swe pt Best Picture. Best A ctor Best D irection . best cinemat when he did I ribute Shakespeare on the screen They should have been giving t ribute to Bran agh for bringing us one of the greatest films of all time
in 7 0 mil. It a shame that Columbia hasn
ography What films were they watching I felt sorry for Bran agh at the O
(b)
Figure 7: Qualitative Results in NLP. Both groups (a) and (b) present results from different explanation
methods for the same example obtained from the IMDB benchmark. In each group, the first row represents the AttaLRP baseline, followed by the PE-only variant in the middle, and finally, our maps at the end.
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E Proofs of Lemmas
Lemma 3.1. For input-level PE transformers, the conservation property is violated when disregarding the positional embeddings' relevancy scores.
Proof of Lemma 3.1 Let be our input representation to the first transformer layer, such that Z = P + F,, where P and F are the token and positional embeddings, respectively. Let I. be the number of layers our transformer. Following the conservation property, the sum of the relevancy
scores at any given layer should uphold:
R - ~ Σ Rg ERE + Rp)
R
(15)
When ignoring Rr, we get the final relevancy attribution map Rimput, such that:
ERU (REE + Rp) # RE = Rimput
(16)
directly violating the conservation property rule
Lemma 3.2. For attention-level PE transformers, our PE-LRP rules satisfy the conservation property.
Proof of Lemma 3.2 Let M be the number of layers in our Transformer, and L the sequence length. We denote (()) the relevancy score of the output at layer l. Beginning with R(A) as the the model's output propagating relevancy backwards to achieve the final explanation map for the input embeddings Rg, we assume that the standard LRP method does not violate conservation, i.e:
(MM) = R()) Re E E [M]:
(17)
Recall that for our PE-LRP formulation, achieve the final explanation map by summing together the semantic attribution Rg, achieved by the standard LRP rules, and the positional relevancy R distributed across the absorbing sinks each attention layer E [M], giving the final relevancy
map Rg + Σ, Rp. We aim to prove the following:
R(M) RR +
R
(18)
Each attention layer in the transformer is computed using rotary attention:
QKT
Vj E [L.] Q3 = Q, K2 R2K,, Rotary Attention (X) Softmax
V. (19)
ddz
Notice that any computation in this layer which involves more than one tensor, is matrix multi- plication function. Adopting the existing baseline, we use the uniform relevance propagation rule, distributing the relevancy evenly between components. Thus, the relevancy scores of Q, K, V, P. with denoting the rotation matrix, is equal, and added together to the relevancy of the attention layer's 's output. The absorbing sink mechanism results in the following:
R (0) RE VI E [M]
R(0 = R((-1) + R
(20)
Following this recursion we would get the exact same result as Eq. 18
Lemma 3.3. For attention-level PE transformers, current LRP attribution rules achieve low faithful- ness, especially when considering positional features.
Proof of Lemma 3.3 We define basic learning problem which relies solely positional features, proving that existing LRP--baeed explanation methods which don't propagate relevance through positional encodings, will not produce faithful explanations. Let us assume we use an auto-regressive transformer model (e.g GPT), with a single causal self-attention with Alibi PE, and the Value projection replaced by an affine transformation (instead of linear layer). Also, for brevity, let us consider scalar input tokens with sequence length of L = 2, denoted by I1, Z2. The final model uses the following keys (K), queries (Q), and values (V):
Vi E [1,2]
Q WQX
K,, WKXi, V,= Wy +
(21)
20

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We apply the Alibi self-attention mechanism, and obtain the final output 0 = (01,
A(i.)) - K.K, vd
+ my (i-ii-1) -). MA = 1, - -2,1 + A2,2
(22)
To prevent the semantic representation from affecting the prediction, an optimal solution to this problem will assign zeros to Wq. WK, namely: Q K
() For the Value projection,
0)
assume: V Wo b, with W.
Relevance propagation. Following our settings, we get:
0 1 0
giving us: Allention( K, K, Ax = 0)
(23)
For the backwards relevancy propagation, the relevancy scores of Altention are distrusted between A and V based on the standard Gradient x Input. Regardless, we now consider how relevancy scores of both terms Rv,
Recore are propagated back the imput xT
Ry + Rg. recall that W, are assigned with zeros. Given that the fundamental e-LRP rule for affine transformations ignores the bias term completely and uses the weights W, as a measure of weighting the relevancy scores, we get that zero relevancy scores are produced for both tokens.
RA Rg. Following the standard LRP rules, the positional terms would be considered a constant, and therefor, 100% of the distribution would be directed to the queries and keys. Given that Wo, WK are assigned with zeros, again get zero relevancy scores being propagated to x.
Given that the relevancy scores propagated back from the attention layer are all assigned with zeros. we will get final attribution map of zeroes, indicating the same level of impact for all tokens. This of course, yields an unfaithful explanation. In contrast, our method makes positional terms attributable. maintaining relevancy scores that would otherwise be zeroed out due to existing rules.
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Conservation Percentage Results
We measure the sum of relevance for DeiT model different capacities: Tiny, Small, and Base. The figure provides clear visualization for the violation of the conservation property, with PE relevancy constituting 16. 16.75%, 22.39%, and 9.22% out of the total relevancy for Tiny, Small, and Base DeiT models, respectively.
1.0
0.8
0.6
0.4
0.2
0.0
Ours
AttmLRP
PE Only
Ours
AttLAR
PE only
Ours
AtnLRD
PE only
Tin
Medium
Small
Figure 8: We assess both the positional relevance and the non con-tooiiional relevance for DeiT models at different capacities, visualizing violation of conservation rule, with high non non- negligible ratio between the entire relevance in the models 'ours' and positional-associatee relevance 'PE Only'.
G Experiments Compute Resources
All experiments were conducted using the PyTorch framework on publicly available datasets and executed on single NVIDIA H100-80GB HBM3 GPU, running for at most 12 hours.
H Experimental Setup
PyTorch is used for conducting all of our experiments. We note that while exploring optimal rules for PE-LRP through theoretical analysis or grid search could further improve empirical results, deliberately chose not to perform hyperparameter tuning for our PE-LRP rules in order to neutralize the impact of hyperparameter search.
We provide the number of randomly chosen samples used for perturbation evaluation, for each dataset:
Imagenet: 12,500 samples
ARC-Fasy 5,200 samples
Wiki-text: 5,000
IMDB: 5,000
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I Statistical Analysis
We have added comprehensive statistical analyses, including variance measures and paired t-tests, for the empirical experiments. For the paired t-tests, we compare each baseline with our method, where each paired comparison is performed on the classification accuracy for each sample across all perturbation steps. We report the full results in Tables 8
Table 6: Perturbation Tests in NLP. Evaluation of LLaMa-2 7B and Tiny-LLaMM, finetuned
IMDB, on pruning and generation perturbation tasks. Att [2] is the LRP baseline. The metrics used are AUAC (area under activation curve, higher better) and AU-MSE (area under the MSE, lower is better).
Model
Method
Generation
Pruning
AUAC f
AU-MSE
AUAC
AU-MSE +
L.aMa-22 7B
IG
0.556 0.03
24.473 + 0.30
0.556 + 0.03
24.438 + 0.30
(P:0.00,T:24.288)
(P:0.00.T:-37.499
(P:0.00,T:24.466)
(P:0.00,T:24.466
LLaMa-2 7B
Slalom
0.606 + 0.03
18.375 + 0.28
0.636 + 0.03
7.315 + 0.27
(P:0.00.T:20.91)
(P:0.00.T:228.644
(P:0.00.T:-27.66) (P:0.00.T:17.82))
LLaMa-2 7B
Att
0.779 0.05
7.629 0.28
0.777 + 0.05
6.548 + 0.30
(P:0.02,T:2.299)
(P.0000,T:-12118) (P:0.00,T66:47) (P:0.00,T:-13.311
LLaMa-2 7B
PE Only
0.771 + 0.08
6.792 = 0.20
0.771 + 0.03
6.823 + 0.20
LLaMa-2 7B
Ours
0.796 + 0.03
6.521 +: 0.19
0.790 + 0.03
6.325 + 0.19
Tiny-LLaMa 7B
IG
0.637 + 0.03
13.745 = 0.24
0.636 + 0.03
13.770 + 0.24
(P:0.00.T:29.255
(P:0.00,T:-33.88
(P:0.00,
(P:0.00,T:-34.544
Tin-LLLaMa-21 7B
Slalom
0.611 0.03
15.408 0.26
0.608 + 0.03
15.666 0.26
(P:0.00,T:22.003)
(P:0.00,,:-30.866)
(P.0.00,T:331.20) (P.0..0,T:20.67))
Tiny-IJaMa-2 7B
AttnLRP
0.803 0.05
8.065 0
0.792 + 0.05
4.030 + 0.22
(P:0.00.T:0.55)
(P:0.01,T:2..49) (P:0.00,T:-29.477)
(P:0.00..:1..1)
T 7B
PE Only
0.788 + 0.03
3,918 + 0.16
0.788 + 0.03
3,947 + 0.16
T 7B
Ours
0.806 0.03
4.915 0.15
0.805 + 0.15
4.082 1 0.05
Table 7: Perturbation Tests in NLP (Zero-Shot). Evaluation of LLaMa-3 8B in zero-shot generation and pruning perturbation tasks for both multiple-choice question answering and Next- Token Prediction (NTP) settings. Metries reported AUAC (area under activation curve, higher is better) and AU-MSE (area under MSE, lower is better). "AttnLRP" refers to the LRP baseline ( 'G' for generation and 'P' for pruning.
Muliipl- Question Answeing
Next Token Prediction
Method
G. A
G. AU--MEE
P. ALJAC
P. AU--MEE
G. ALACT
G. ALL-MEE
P. AAUAAC
P. AL-MSE 1
SHAP
0.201 = 0.04
141.757 + 0.71
0.282 0.04
147.229 0.71
IG
0.851 + 0.02
119.881 + 0.50
0.330 0.02
123.212 0.50
0.481 0.34
40.881 0.50
0.481 + 0.04
40,700 0.58
(P:0.00T::446)
(P:0000TT-64.00 09)
(P.0.00.T4222 22)
(3:000.T-63.33
(6:0.00.:16645)))
(F:0.88.7-0.05)
(P:0000(::16466)
(P-096T:-405))
AttLRP
I13033 à: 0.00
66.299 2: 0.56
0.354 :: 0.04
68.856 1: 0.57
0.559 11.44
41.704 0.58
0,559 1: 0.04
42,003 2: 11.8
(P:0.00T::4.44
(P:300,T:90 9.79)
(P0.00.1:5443 43)
(P:0000T: (0.33)
(P:0.06.:: T.1 1.199
(P.0000::
(P:0006,1:187))
(P:0.000::21
PEy
I13374 + 0.00
61,014 = 0.47
0.961 0.04
63,141 + 8.47
0.307 0.31
038.388- 0.58
0.655 0.04
40.800 = 1138
(P:0.01.::255))
(P:0.0I.T2.600)
(P:0.01.T2.669
(P:3.00T::2.5
(P:0.00.::1122
(P:6.00TT-3
(P:0:00T::1122
(P:0.00:::33200 3.20)
Ours
0,377 0.04
61.265 0.47
0.368 0.04
65.
0.562 0.58
40.474 0.34
0.561 0.04
40.735 0.58
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Table Perturbation Tests for DeiT Variants on ImageNet. AUC results for predicted class, Higher (lower) is better for negative (positive).
M. Size
Method
Negative
Positive
Predicted
Target
Predicted
Target
Base
Att
52.185 + 0.08 (P:0.00.T:20.60)
47.516 + 0.01 (P:0.00.T:14.63)
10.784 + 0.01
8.032 + 0.00
(P:0.00,T:-9.00) 9.918 + 0.03
(P:0.00,T.7.78)
Base
Ours
54.970 + 0.03
50.174 + 0.02
9.237 + 0.02
Small
AttnLRP
50.662 + 0.03
45.105 = 0.02
10,511 + 0.03
9.761 + 0.02
(P:0.00.T:22.20) 53.482 + 0.03
(P:0.00.T:17.73) 47.948 + 0.02
(P:0.00.T:-16.55)
(P:0.00.T:-14.22)
Small
Ours
9.135 = 0.03
8.477 0.02
Tiny
AttLRRP
43.832 + 0.03
37.499 + 0.02
2.796 + 0.03
2.503 + 0.02
(P:0.00,T:53.1555
(P:0.00,1:22.366) (P:0.00,T:45.700
(P:0.00,T:200.99)
Tiny
Ours
50.1241 + 0.03
42.567 1 0.02
3.579 = 0.03
3.214 1 0.02
The results demonstrate that all differences reported in the paper are statistically significant, as verified using paired t-tests. We observe p < 0.03 figures across experiments, with most p-values lower than 10 The variance is consistently low, and paired t-tests yield small p- p-values, while t-scores are aligned with performance trends.
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J Perturbation Test for Vision Transformer: EpslionGamma vs. AlphaBeta
To evaluate the robustness of our method against other LRP-based rules (Tab. we conduct additional perturbation experiments for the vision transformer, comparing the settings adopted by AttnLRP (2). which are considered optimal and utilize mix between the 7 and rules, with the well-adopted a-B propagation rule, adopted in various prominent XAI papers (e.g. [4).
Table 9: Perturbation Tests for DeiT Variants on ImageNet, comparing y-c, and a- LRP rules, for AttnLRP and our method.
M. Size
Method
Negative f
Positive Predicted
Predicted
Target
Target
Base
AttnLRP (o, (3)
44.612
40.542
9.172
8.515
Base
AttnLRP (c, 7)
52.185
47.516
10.784
8.032
Base
Ours (a,, 9))
61.974
55,237
43.557
39.499
Base
Ours 7)
54.970
50.174
9.918
9.237
Small
AttaLRP (a, (8)
45.698
40.890
9.822
9.048
Small
AunLRP (c,7)
50.662
45.105
10.511
9.761
Small
Ours (a; ()
62.963
55.114
33.651
30.158
Small
Ours (c)
53.482
47.948
9.135
8.477
Tiny
AttnLRP (a, (8)
31,297
26.962
11,301
9.891
Tiny
AttLLP (c,7)
43,832
37.499
2.796
2.503
Tiny
Ours (o,, B)
56.925
46.835
29.049
24,468
Tiny
Ours (e,
50.1241
12.567
3.579
3.214
The results demonstrate that using the settings presented in AttLRP 2 (c, 7) provides the optimal
performance for both methods. We underline that while the alpha-beta rule manages to achieve higher performance for negative perturbation for method, it is accompanied by drastic tradeoff with positive perturbations, which implics that the attribution map is coupled with high level of noise.
K Perturbation Test for Quantized Large Language Transformer
To evaluate the robustness of our method on quantized models (Tab. T0p. we conduct additional per- turbation experiments for quantized version of LLaMa 2-7B finetuned the IMDB classification dataset.
Table 10: Perturbation Tests in NLP. Evaluation of quantized LLaMa-2 7B, finetuned on IMDB, on pruning and generation perturbation tasks. AttaLRP (2) is the LRP baseline. The metries used are AUAC (area under activation curve, higher is better) and AU-MSE (area under the MSE, lower is better).
Model
Method
Generation
Pruning
AUAC
AU-MSE +
AUAC AU-MSE
M.La 7B Quantized
AtmaIR
0.774
11.348
0.767
10.067
LLaMa-2 7B Quantized
PE Only
0.758
10.730
0.758
10.774
LLaMa-2 7B Quantized
Ours
0.785
10.137
0.778
9.685
The results presented in Table I0 demonstrate that quantization does not affect the effectiveness of our method, which consistently outperforms the baseline. particular, our approach improves the AU-MSE score in the generation scenario by 10.6%.
L Broader Impacts
Our work proposes a novel XAI technique that enhances the explainability of transformer models. XAI plays a critical role ensuring the safe and responsible deployment machine learning systems, particularly in high-stakes domains such as healthcare, finance, and law. It helps users and researchers understand, trust, and effectively audit model decisions. particular, XAI facilitates the detection of biases, identification of failure modes, and debugging of unintended behaviors. By providing more
25

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accurate and faithful explanations for transformer based models, our method contributes to greater transparency and accountability in DL systems. For all of those reasons, believe this work will positively impact both the research community and practical applications by enabling safer and more interpretable use of powerful language models.
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Question: Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
Justification: NA
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non stansrdd components.
Please refer to LLM policy (https://neur))) http:/ppps..c/ooooeeeee/e/////// for what should or should not be described.
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Reviews:
[{"review_id": "QnzBNXQQ5A", "reviewer": "Reviewer_xvHC", "summary": "The work proposes an extension to Layer-wise relevance propagation (a feature\nattribution approach) for Transformer models, which encompasses rules to\nexplicitly assign importance to positional embeddings. Specifically, the work proposes rules for five approaches to positional embedding: learnable, sinusoidal, rotary, and relative positional embeddings, as well as ALiBi.\nThe work provides some theoretic motivation regarding the conservativity and fidelity of the approach compared to AttnLRP.\nThe work compares the proposed approach to AttnLRP in empirical perturbation,\nablation, and segmentation experiments on vision data using DeiT, as well as on\nnatural language data using LLaMa-2-7B, LLaMa-2-7B quantized, Tiny LLaMa, and\nLLaMa-3-8B.\nThe work also provides some qualitative comparison on both vision and language data.", "questions": "1. The quantitative experiments do not include any errors over the trials. I\n   could also not find the number of trials conducted for these experiments.\n   Regarding my following two questions, there might seem to be some cases in\n   which there is no clear benefit from explicitly attributing positional\n   embeddings. These points make the results somewhat inconclusive, as the work\n   does not provide any analysis as to when the proposed approach should be\n   used, and when it should not. The qualitative results also do not seem to\n   give a clear difference. The work would be much more convincing if it could\n   provide the number of trials, and conduct a Wilcoxon-Rank-Sum test to\n   provide more conclusive evidence in favor of attributing positional\n   embeddings. It would be immensely helpful for the community to understand\n   when and why we should consider positional embeddings for LRP, to which I\n   was not entirely able to convince myself based on the results.\n\n2. For Table 1, the work states that the proposed approach lags behind for the\n   positive perturbation, but performs better in the negative perturbation. This\n   seems to suggest that for some setups, it is better to not regard the\n   positional embedding. What are the implications of this observation? How\n   significant are these results?\n\n3. In Table 3, PE seems to outperform both AttnLRP as well as AttnLRP+PE on\n   Tiny LLaMa. However, adding AttnLRP+PE does not seem to improve\n   significantly over AttnLRP. Do you have any idea why this could be the case?\n\n4. I am sceptical of how useful Lemma 3.1 is. As far as I understand, AttnLRP\n   assumes the feature attribution only for the sum $R_E + R_P$, where the\n   positional part is simply by design part of the input. I do understand that\n   there are some limitation to not model the positional embedding explicitly,\n   which this work empirically evaluates. However, I do not find this\n   conservativity argument meaningful for understanding the distinction between\n   handling and not handling positional embeddings. I also do not think that\n   Lemma 3.2 is very useful, as Eq. 9 is per definition conservative.\n\n5. Lemma 3.3 claims that in general, current LRP attribution rules (i.e.,\n   AttnLRP) provide low faithfulness for attention-level PE transformers with\n   respect to positional features. This lemma is not proven, but rather only a\n   specific extreme-case example is provided. While the lemma could be\n   reformulated, I do not think it makes a particularly convincing argument as\n   to when it is useful to regard positional embeddings, and when it is not.\n\n\nI would like to emphasize that I think it is helpful to consider positional\nembeddings in LRP. However, I think that there are use-cases for which\npositional embeddings do and do not matter. This works in its current state\nseems to be convinced that positional embeddings should always be regarded,\nalthough empirical experiments do not seem fully conclusive on this idea. The\nwork would be much stronger if it analyzed the idea that the usefulness of\npositional information might be conditional, and explored cases for which it\nmight not. With the current state, I am leaning towards reject, but could be\nconvinced the empirical experiments would be analyzed for statistical\nsignificance, as well as regard cases in which attributing positional\nembeddings might not be useful.\n\nMinor:\n- I think it might be more descriptive to call the \"Lemmas\" propositions\n  instead. Lemmas usually aid in the proof of some Theorem, which is not the\n  case in this work.\n- I think the notation in Appendix E is overly simplified, especially\n  considering the sums without subscripts. I would also recommend to use the `\\text{input}` etc.\\ for descriptive subscripts.", "limitations": "The work states that it is not a limitation that it does not analyze existing LRP rules.\nWhile I agree with this statement, I do not think it provides any insights into\nthe actual limitations of this work.\nIn its conclusion, the work describes a drastic improvement of XAI when\nconsidering positional information. I think a more interesting limitation would be to describe cases where this positional information could be even misleading.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- AttnLRP provides a powerful feature attribution approach. Improving this\n  approach with an explicit handling of the positional embedding is a useful\n  contribution to the field.\n- The work touches a multitude of positional embedding approaches, which\n  significantly aids in its practicality.\n- The work uses established evaluation metrics for feature attribution, namely\n  perturbation and ground-truth segmentation.\n- The work follows a clear structure and is well-written.\n\n### Weaknesses\n\n- The empirical experiments are partly inconclusive, given that for some\n  metrics, the baseline wins, and for others, the improvements seem to be\n  marginal. However, there are no error bars or number of trials reported,\n  which would aid in testing for statistical significance.\n- The work argues that AttnLRP is not conservative unless positional embeddings\n  are attributed. I do not understand why this argument matters for the\n  distinction between considering and not considering positional embeddings.\n  Even if one would deem this argument valuable, both approaches seem to be\n  conservative in their own perspective.\n- The work claims to present some theoretical evidence that the omission of\n  positional embeddings in attribution gives low fidelity. However, this is not\n  proven, but only an extreme-case example is provided. The claim is also not\n  consistent with the empirical results, which indicate that in some cases,\n  fidelity can be higher when omitting the attribution of positional\n  embeddings.", "was_revised": true, "final_justification": "With the authors' clarifications and updated error bars for the empirical experiments, as well as the statistical tests, assuming the authors intend to update all the results where this applies and supply the number of examples in the paper, I am confident to raise my score to Borderline Accept.\n\nHowever, I currently do not feel confident to raise my score beyond this, as the theoretical analysis is lacking in formality and meaning.", "rebuttal": "We are grateful for the reviewer’s feedback.\n\n> W.1 The empirical experiments are partly inconclusive, given that for some metrics, the baseline wins, and for others, the improvements seem to be marginal.\n\n\nWe respectfully disagree with the reviewer’s assessment and maintain that our empirical results clearly demonstrate the importance of attributing positional components for achieving high XAI performance. In the NLP domain, using attention-level PE, our method consistently outperforms Attn-LRP across all model capacities and evaluation settings. Notably, on the IMDB classification task (Table 3), our approach improves the AU-MSE score in the generation scenario by a substantial margin, ranging from 14.5% to 51.41%.\n\nWhile we occasionally observe only marginal improvements on certain metrics, these cases are consistently accompanied by significant gains on complementary metrics for the same model capacity. In some perturbation experiments for vision tasks, our method exhibits a slight decrease in positive perturbation performance but consistently achieves substantial improvements in negative perturbation performance—highlighting its overall advantage.\n\n\n\n\n \n> W.1. & Q.1.1 there are no error bars or number of trials reported.\n\nThank you for pointing out this shortcoming. We will add the number of runs, SD, and paired t-tests to all experiments. Reassuringly, all differences we pointed out in the paper are significant, verified using the paired t-tests. We report p<0.03 figures for our experiments, mostly with p values lower than 10^-4. See, for example, Tables A,B  below, which provide the analysis for Tables 3,5 in the paper, respectively.\n\n\n\n\n\n\n\n>W.2. … argues that AttnLRP is not conservative unless positional embeddings are attributed. I do not understand why …\n\nThe conservation property is fundamental to LRP's interpretability guarantees, ensuring attribution magnitudes remain proportional to the function’s output, and is highly regarded as a crucial factor for achieving faithful explanations [1, 2, 3]. The conservation property by itself is not a consideration for the distinction between considering and not considering positional embeddings, but rather a theoretical justification to explain improvement for empirical results.\n\n\n\n\n> W.3. The work claims to present some theoretical evidence that the omission of positional embeddings in attribution gives low fidelity. However, this is not proven, but only an extreme-case example is provided. The claim is also not consistent with the empirical results, which indicate that in some cases, fidelity can be higher when omitting the attribution of positional embeddings.\n\n\nWe respectfully disagree with the claim that our fidelity argument is not proven. It is supported by a counterexample, a well-established and effective proof technique, and follows the same standard used in prior work in the domain (e.g., Chefer, Lemma 3; AttnLRP, Appendix A.2.1). We also do not consider this proof to fall under the category of an “extreme case.” Rather, it reflects the simplest and most controlled setting, demonstrating that even in such basic scenarios, fidelity issues can arise. \n\n\nWe would like to emphasize that employing simplifications to derive theoretical insights is a widely accepted practice in deep learning research. In particular, theoretical analyses of faithfulness in LRP methods (especially in the context of Transformers) are exceptionally rare and inherently difficult to formalize. From this perspective, we view Lemma 3.3 as a meaningful and technically valuable contribution. \n\n\nWith regard to consistency with empirical results, we emphasize that our theoretical claims on faithfulness are solely focused on attention-level PE, which is widely used in LLMs. In the experiments we conducted with attention-level PE (see Tables 3 and 5), the results are highly consistent with our theory. For example, when incorporating our PE-LRP rules into LLaMA models, our approach improves the AU-MSE score in the generation scenario by 14.5% for LLaMA 2-7B, 10.6% for LLaMA 2-7B Quantized, and 51.41% for Tiny-LLaMA. Notably, AttnLRP never outperforms both of our methods (PE-LRP and PE only) in any setting (see Tables 3 and 5) in the attention-level regime.\n\n\n> Q.1.2. .. there might seem to be some cases in which there is no clear benefit from explicitly attributing positional embeddings. ..These points make the results somewhat inconclusive\n\nWe clarify that our main results are not inconclusive. Our PE-LRP rules are particularly effective when incorporated in the attention-level, and in those settings (see Tables 3 and 5 in our paper), our method consistently outperforms Attn-LRP, with no single case in which Attn-LRP achieves the best score. \n\n\n> Q.1.3. These points make the results somewhat inconclusive, as the work does not provide any analysis as to when the proposed approach should be used, and when it should not. The qualitative results also do not seem to give a clear difference…\n\n\nWith regard to determining when and why to propagate PE is necessary, our empirical results demonstrate that PE attribution is crucial for explanation quality across the board - the 'when' is always for models with attention-level PE (our NLP experiments) and almost always for Vision, and the 'why' is that PE information is already thoroughly integrated throughout the model's representations and therefore must be attributed. Attempting to isolate specific conditions where PE attribution might or might not be beneficial goes beyond the paper's scope. \n\n\n> Q.2. For Table 1, the work states that the proposed approach lags behind for the positive perturbation, but performs better in the negative perturbation. This seems to suggest that for some setups, it is better to not regard the positional embedding. What are the implications of this observation? How significant are these results?\n\n\nThe reviewer misinterprets the mixed positive/negative perturbation results reported for vision classification task (Table 1). For a few cases, our approach showed a minor decrease in positive perturbation performance while achieving substantial improvement in negative perturbation performance. This trade-off is favorable - the slight positive perturbation decrease is outweighed by the significant negative perturbation gains, representing an overall improvement in explanation fidelity. These cases do not support the reviewer's broader claim about settings where disregarding positional embeddings is beneficial; rather, they demonstrate our method's effectiveness. These results mainly show that our approach is better at identifying truly important features, but it may have some noise in its ranking. \n\nAdditionally, it is important to note that our work does not perform hyperparameter tuning for the LRP hyperparameters or the selection of LRP rules, and thus our setup is likely not optimal and could be readily improved through such procedures.\n\n\n> Q.3. In Table 3, PE seems to outperform both AttnLRP as well as AttnLRP+PE on Tiny LLaMa. However, adding AttnLRP+PE does not seem to improve significantly over AttnLRP. Do you have any idea why this could be the case?\n\n\nOne possible explanation is that in Table 3, when evaluating XAI performance for sentiment analysis, the model relies heavily on opening statements and closing conclusions to make its prediction. As such, most of the prediction is attributed to the positional components of tokens at the beginning and at the end of the prompt.\n\n\n\n\n> Q.4.1. I am sceptical of how useful Lemma 3.1 is...\n\n\nFor Lemma 3.1, referring to input-level PE, such as in vision transformers, the image patches undergo convolution (or linear layers)  first, then PEs are added to these patch embeddings before entering the transformer layers. This means PE is not simply \"part of the input by design\" - it's a separate additive component that AttnLRP treats as transparent. Moreover, while some methods choose to focus only on attributing the sum of positional and semantic relevance, without propagating relevance through the entire model as LRP is designed to do, this decision is unjustified and purely heuristic. Our approach provides a clear rationale for this design choice and defines how positional encodings should be treated. \n\n> Q.4.2. However, I do not find this conservativity argument meaningful for understanding the distinction between handling and not handling positional embeddings. \n\nAssume, for example, that in a ViT model, patch embeddings (denoted by E) are extracted via a convolutional layer, and then PEs (P) are added to form Z=E+P, which is then passed into the Transformer. If we aim to propagate relevance back to the input space of the model at the pixel level (what LRP is designed for), the existing methods typically propagate relevance only through E, not through P. As a result, the relevance R(E) is propagated through the convolution back to the input, but any positional relevance R(P) is lost.\n\nAdditionally, for input-level PE, simply applying LRP to the sum positional and semantical relevance​ can lead to a loss of relevance, as negative positional relevance may override positive semantic relevance at each coordinate, and vice versa.\n\n> Q.4.3. .. do not think that Lemma 3.2 is very useful, as Eq. 9 is per definition conservative.\n\nWhile there are specific cases (due to a combination of architecture, particular LRP rules, and the unjustified decision to stop relevance propagation before reaching the input space) where ignoring PE does not violate conservation, there are also clear cases where conservation is violated, such as the example discussed in our response to Q.4.2. In general, when relevance is propagated all the way to the input space rather than stopping at the point where PE is added, ignoring PE leads to a violation of conservation. This is critical, as propagating relevance to the final input space is a fundamental principle of LRP."}, {"review_id": "8xbPWOFlPj", "reviewer": "Reviewer_Qg1b", "summary": "This paper presents and addresses a limitation in the explainability of transformer-based deep learning models: The handling of positional encodings (PEs) in Layer-wise Relevance Propagation (LRP) methods. The authors argue that existing LRP-based XAI ignores the role of positional encodings, violating the conservation property and obscuring important positional and structural attributions. To tackle the problem, they reformulate the input space and propose new propagation rules. The effectiveness of the approach is evaluated on vision and NLP tasks.", "questions": "Questions: Can you elaborate on the baselines used for comparison: What was used as the input for plain LRP? Did you backpropagate to E_i or E_i + P_k for the standard learned or sinusoidal embeddings.?\n\nWhy can't we just use LRP from the sum E_I + P_K (for standard additive embeddings)? \n \nWhat was the motivation between just adding (as far as I understand) semantic and positional importance?", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 1, "originality": 3, "strengths_and_weaknesses": "## Strengths\n* **New problem suggestion.** The paper lays out a previously unconsidered issue with LRP methods\n* **Theoretical Foundation.** The authors provide formal analysis of their rules in Lemma 1 and 2. The proofs are referenced to the appendix but the statements in the main paper are clear.\n* **Experiments span two domains and feature relevant models** such as DeiT variants and the Llama models in realistic domains with quantitative and qualitative metrics\n* **Open-Source Implementation.** The paper provides a complete codebase, supporting replicability and adoption.\n\n## Weaknesses \n\n* **Limited Scope: Heavy reliance on Transformers and LRP**: The method is tightly coupled to LRP for Transformers. While the extension to other LRP variants is mentioned, its generality outside the AttnLRP framework is assumed. Broader applicability or adaptations to alternative XAI paradigms (e.g., SHAP, counterfactuals, or other propagation-based methods) are not explored.\nTo motivate this, the authors argue that LRP is the most reliable technique for transformers (l.31). I don’t think that the reality is that simple. Some current works, e.g., Leemann et al (2024) investigate surrogate model explanations for transformers such as SHAP and LIME and find their performance often comparable to LRP in common metrics such as insertion or deletion curves. Thus, I think the scope and potentially impact could be extended by generalizing the approach to other XAI techniques as well.\n\n* **Motivation for specific rules** LRP decompositions are not unique for a given function. While the rules are presented, there is no explanation for why exactly the given form was chosen. Even for the simple additions, their can be 0-LRP, epsilon-LRP or even Gamma-LRP Rules (Montavon, 2019). There should be some discussion about alternative rules and why they were discarded.\n\n* **Relevance and Signficance.** I don't fully understand the signficance of the distinction between positional and semantic importance yet. Why don’t we just redefine the input to be $E_k$ + $P_i$ where $E_k$ is the semantic token embedding and $P_i$ is positional embedding $i$, and backpropagate to this input quantity using LRP? This should fulfill the conservation properties and attribute to token k at position I, which is exactly what we want? The paper claims to manage to separate positional relevance from semantic relevance, but doesn't discuss why it matters in the end.\n\n* **Some confusion about notation.** Writing of Equation (4) is a bit confusing. While the input space is a set of vectors in (3), here in (4) it seems to be a set of tuples, however the size of the tuple and which combinations are possible is still unclear to me. Please elaborate.\n\n* **No measures of dispersion.** Unfortunately, the results in Table 1-3 feature no measures of dispersion making it hard to assess their significance.\n\n\n\n------------------\n**References**\n\nGrégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Wojciech Samek & Klaus-Robert Müller. Layer-Wise Relevance Propagation: An Overview, 2019.\n\nLeemann, Tobias, Alina Fastowski, Felix Pfeiffer, and Gjergji Kasneci. Attention Mechanisms Don’t Learn Additive Models: Rethinking Feature Importance for Transformers.\" Transactions on Machine Learning Research. 2024.", "was_revised": true, "final_justification": "I am maintaining my prior assessment.", "rebuttal": "We thank the reviewer for their detailed comments and suggestions and provide responses to the raised concerns below.\n\n\n> W.1. Limited Scope.\n\nWe respectfully disagree with this statement. Transformer explainability, especially for LLMs, is a crucial emerging discipline, with LRP-based methods being at the center of this domain. Moreover, Attn-LRP [1] shows that LRP-based XAI methods consistently outperform other XAI methods such as KernelSHAP, IG, and others. We underline that at least two dozen papers proposing LRP techniques for transformers have been previously published at top-tier conferences, such as ICML and ICLR. Notable examples include [1-3].\n\nTo establish LRP's superiority in XAI performance, we conduct a series of experiments using the baselines suggested by the reviewer (Leemann et al, IG, SHAP). The results, presented in Tables A and B, mentioned in the response to reviewer jDLx,, show that our method consistently outperforms the baselines. It is demonstrated that methods which are not LRP-based consistently show inferior performance across tasks. Moreover, our method consistently outperforms Attn-LRP, with no single case in which Attn-LRP achieves the best score. In particular, our approach improves the AU-MSE score in the generation scenario by 14.5% for LLaMA 2–7B, 10.6% for LLaMA 2–7B Quantized, and 51.41% for Tiny-LLaMA.\n\n\n\n\nAdditional rows for Table 3 from our paper (perturbations in NLP under the finetuning regime), including variance values (shown after the ± symbol). The table reports paired t-test results comparing each baseline to our method, such that each element in the pair  the comparison is made for the classification accuracy, for each sample, across all perturbation steps. Results are based on 5,000 randomly sampled reviews from the IMDB test set. Please see Table A in response to reviewer jDLx.\n\nAdditional rows for Table 5 from our paper (perturbations in NLP under the zero-shot regime), including variance values (shown after the ± symbol). For multiple-choice question setting, we utilize the ARC-easy dataset, containing 5200 samples. For next token prediction task setting, we use the Wiki dataset, containing 5000 uniformly selected samples . Please see Table B in the response for reviewer jDLx. \n\nWe also note that SHAP is also significantly more computationally involved due to their model-agnostic nature, which requires surrogate modeling and repeated inference, resulting in a total runtime increase of approximately 5 times more  in our experiments.\n\n> W.2. Motivation for specific rules LRP.\n\n\nWhile exploring optimal rules for PE-LRP through theoretical analysis or grid search could further improve our empirical results, we deliberately chose not to perform hyperparameter tuning for our PE-LRP rules in order to neutralize the impact of hyperparameter search. \n\nIn our experiments, we followed the same guidelines reported as optimal in AttnLRP, specifically a combination of the $\\epsilon$-rule and the $\\gamma$-rule. We agree with the reviewer that a broader discussion of alternative relevance propagation rules is warranted. To address this concern, we now include quantitative results on perturbation tests in vision tasks using the alpha-beta rule, demonstrating the significant performance degradation it causes in our setup, which is based on Attn-LRP.\n\n\n**Table C:** The table demonstrates that using the settings presented in AttnLRP (epsilon, gamma) provides the optimal performance for both methods. We underline that while the alpha-beta rule manages to achieve higher performance for negative perturbation for our method, it is accompanied by a drastic tradeoff with positive perturbations, which implies that the attribution map is coupled with high level of noise.\n\n| M. Size | Method                         | Negative ↑ (Predicted) | Negative ↑ (Target) | Positive ↓ (Predicted) | Positive ↓ (Target) |\n|---------|--------------------------------|----------------------|-------------------|----------------------|-------------------|\n| Base    | AttnLRP (α, β)                 | 44.612               | 40.542            | **9.172**            | 8.515             |\n| Base    | AttnLRP (ε, γ)                 | **52.185**           | **47.516**        | 10.784               | **8.032**         |\n|         |                                |                      |                   |                      |                   |\n| Base    | Ours (α, β)                    | **61.974**           | **55.237**        | 43.557               | 39.499            |\n| Base    | Ours (ε, γ)                    | 54.970               | 50.174            | **9.918**            | **9.237**         |\n|||||||\n| Small   | AttnLRP (α, β)                 | 45.698               | 40.890            | **9.822**            | **9.048**         |\n| Small   | AttnLRP (ε, γ)                 | **50.662**           | **45.105**        | 10.511               | 9.761             |\n|         |                                |                      |                   |                      |                   |\n| Small   | Ours (α, β)                    | **62.963**           | **55.114**        | 33.651               | 30.158            |\n| Small   | Ours (ε, γ)                    | 53.482               | 47.948            | **9.135**            | **8.477**         |\n|||||||\n\n\n\n\n\n\n> W.3. … distinction between positional and semantic importance  …\n\n\nWe do not claim to fully disentangle positional relevance from semantic relevance. Rather, our aim is to highlight the existence of a positional relevance component—one that has been largely overlooked but is shown through our analysis to be crucial for achieving strong XAI performance. We first want to clarify that in our experiments, we included a separate setting (\"PE Only\") as an ablation to better understand and isolate the contribution of position-associated relevance, and in practice, both positional and semantic relevance were computed through the same LRP backward path.\n\nSecondly, we believe that the reviewer oversimplifies the problem. For example, in attention-level PE, such as RoPE (popular in LLMs like LLaMA and Qwen), PE is integrated into all attention layers, and thus cannot be treated as part of the input. Additionally, this type of PE is relative rather than absolute, so simply redefining the input is not feasible. Moreover, without dedicated LRP rules, relevance conservation will be violated, as PE components involve many nonlinearities (for example, RoPE includes trigonometric functions). Our work addresses all these challenges with both theoretical and empirical analysis.\n\n\n> W.4. Some confusion about notation.\n\n\nEquation 3 presents the input space as defined in previous work, while Equation 4 introduces our reformulation. The intuition is simple: previous methods define the input space using only the word embeddings (a list of vectors in $\\mathbb{R}^D$). We argue that this is not precise for Transformers, as PE is also part of the input. In short, a transformer with PE operates over ordered sequences rather than unordered sets, so defining the input space without position does not accurately reflect the problem. Therefore, the input space should be defined as token-position pairs, consisting of both the semantic information representing the words (as in prior work) and the positional information, which we explicitly include in our reformulation ($\\forall k \\in [K], j \\in [L]: P_{j,k}$). \n\n> W.5. No measures of dispersion. \n\n\nWe thank the reviewer for the suggestion, which we address with dedicated experiments. To ensure the robustness of our results, we report variance across runs and conduct paired t-tests to support statistical significance. The results will appear in the camera-ready version. See examples below for perturbation tests in NLP. We demonstrate that the variance is consistently low, and paired t-tests yield small p-values with t-scores aligned with performance trends.\n\nPlease see Tables A and B mentioned in the response for reviewer jDLx\n\n\n> Q.1: … What … input for plain LRP? Did you backpropagate to E_i or E_i + P_k ...?\n\nWe first want to clarify that our main contribution focuses on attention-level PE, not input-level PE. Attention-level PE is widely used in modern LLMs and has a strong impact on the behavior of each attention layer. In this setting, our results are the strongest, and our theoretical analysis stands out (see Lemma 3.3).\n\nFollowing this perspective, our contribution is to define LRP rules that enable relevance to be propagated through PE layers such as RoPE, ALiBi, and input-level PE.\n\nFor input-level PE, which is the simpler case and not our main contribution, we define the input space as the concatenation of $E_i$​ and $P_k$​. We then propagate relevance to both components and sum the positive values along the corresponding feature dimensions.\n\n\n> Q.2: Why can't we just use LRP from the sum E_I + P_K (for standard additive embeddings)?\n\nFirst, the main part of our work, which is more complex, novel, and effective: our PE-LRP rules for attention-level PE used in LLMs.\nFor input-level PE, simply applying LRP to the sum $E_i + P_k$​ can lead to a loss of relevance, as negative positional relevance may override positive semantic relevance at each coordinate, and vice versa. More importantly, since LRP is designed to propagate relevance all the way to the input space, stopping relevance propagation at the path embeddings in ViT models results in ignoring the pixel-space and producing explanations based only on the patch-space (and ignoring the entire computation of the patch embedding). This contradicts the core principle of LRP, which requires relevance to be propagated to the actual input."}, {"review_id": "Y3xHmmYnzO", "reviewer": "Reviewer_jV3L", "summary": "This paper approaches the LRP based explainable methods for transformers and extend this method by assigning relevance for the positional encodings, one of the important and key components of the transformers. This paper proposes theoretically grounded relevance rules to propagate the attributions for the positional embeddings.", "questions": "1. One of the questions that arise are the situations where positional embeddings are less informative in the predictive performance for tasks such as classification?", "limitations": "Yes, the authors did mention the limitations in Section 6.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. One of the major strength of this paper is reformulation to include the positional embeddings theoretically.\n2. The authors mention that assigning relevance to the positional parts can be decoupled and applied to any LRP based framework.\n3. The empirical results shows the effectiveness of assigning the relevance to PE parts specially for the segmentation tests.\n\nWeaknesses\n1. This work is based on the premise that LRP based explainable methods are most effective model specific XAI method. It would be helpful if authors put references for this claim.\n2. This work seems an incremental work on the AttnLRP with additional considerations of positional encodings. This simply appends the positional relevance to that of Attn LRP. The interactions between the attention layers and tokens needs to be explained.\n3. In quantitative analysis, positive perturbation tests for DeIT variants shows Attn LRP performing better for Base and Tiny models. How does the removal of relevant pixels changes the positional understanding, eg: it is understandable that removing relevant pixels does changes the prediction performance but how does the relevance is distributed in context of the positional encodings and propagation rules. \n4. The evaluation is mostly dependent on the perturbation and segmentation based tests. Human based tests might have helped. The quantitative test are mostly acts as proxies.", "was_revised": true, "final_justification": "Most of my concerns were addressed.", "rebuttal": ">W.1. The premise that LRP based explainable methods are most effective model specific XAI method. \n\nWhile we acknowledge that no single XAI method can be definitively labeled as universally superior, we maintain that LRP shows the most promise for our specific application. Although this conclusion arises from the results of the Attention LRP paper, to support this position, we conducted comparative experiments against established methods including IG, SHAP, and [Leeman at al], which consistently demonstrated LRP's superior performance in our evaluation metrics. Please see Tables A and B mentioned in the response for reviewer jDLx.\n\n>W.2.1. Simply appends the positional relevance to that of Attn LRP. \n\nWe respectfully disagree that our work is incremental. In the LRP literature, each paper focuses on improving different aspects. For example, [1], presented at ICML, focuses on softmax and LayerNorm, while attnLRP [2], also presented at ICML, focuses on attention layers. Our method revisits the concept of attributing PEs and propagating relevance through PE layers, and demonstrates both empirical and theoretical importance, especially for LLMs such as LLaMA3. We consider this a major advancement, as XAI tools for such models are extremely important for the community.\n\n> W.2.2. The interactions between the attention layers and tokens needs to be explained.\n\nRegarding the comment “The interactions between the attention layers and tokens need to be explained,” could you please elaborate? In Section 2.1, we discuss how attention is computed using RoPE and ALiBi, and in Section 3.2, we explain how relevance should be propagated through the RoPE and attention layers.\n\n\n\n\n>W.3. In quantitative analysis, positive perturbation tests for DeIT variants shows Attn LRP performing better for Base and Tiny models. How does the removal of relevant pixels changes the positional understanding, eg: it is understandable that removing relevant pixels does changes the prediction performance but how does the relevance is distributed in context of the positional encodings and propagation rules.\n\n\nThe reviewer seems to ask what would be the effect of removing relevant pixels for prediction on the distribution of positional and semantic attribution maps. The question should be asked backwards, how do the positional and semantic attribution maps differ in terms of the order of the most relevant pixels.\n\nThe conclusion should be drawn backwards, how does positional and semantic understanding (e.g. attribution maps) affect the resulting order of which pixels are most relevant for classification. \n\nWe refer the reviewer to our qualitative results section (line 296), where we discuss the difference between positional and semantic attributions. In general, for a few cases, our approach demonstrates a minor decrease in positive perturbation performance while achieving substantial improvement in negative perturbation performance. These results mainly show that positional attribution facilitates identifying truly important features, but at the cost of giving higher relevancy scores to additional tokens, which may have been critical for the computational flow (for example for storing global information regarding the prediction), but are not by themselves crucial for prediction. \n\n\n>W4. The evaluation is mostly dependent on the perturbation and segmentation based tests. Human based tests might have helped. The quantitative test are mostly acts as proxies.\n\nWe respectfully disagree with the reviewer, as there is no definitive and reliable way to evaluate XAI performance using human-based testing. \n\n\n>Q1. One of the questions that arise are the situations where positional embeddings are less informative in the predictive performance for tasks such as classification\n\nOur results strongly suggest that for most situations, positional embeddings are crucial for high-quality XAI performance. PE information is already thoroughly integrated throughout the model's representations and mixed with semantic representations. Attempting to isolate specific conditions where PE attribution might or might not be beneficial goes beyond the paper's scope.\n\n___\n\n**References:**\n\n[1] XAI for Transformers: Better Explanations through Conservative Propagation. Ali et al. ICML 2022\n\n[2] AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers. Achtibat et al. ICML 24\n\n[3] Transformer Interpretability Beyond Attention Visualization. Chefer et al. CPVR 2021"}, {"review_id": "BryvidB6OQ", "reviewer": "Reviewer_jDLx", "summary": "This paper proposes Positional-Aware Layer-wise Relevance Propagation (PA-LRP), a novel explainability method for Transformers that addresses a key limitation in existing LRP-based approaches—the omission of positional encodings (PE) in attribution. The proposed method differs from prior work in two main ways: (1) it reformulates the input space to incorporate both token and positional information, and (2) it introduces the first LRP rules specifically designed to propagate relevance through standard PE schemes, including Learnable, Sinusoidal, and Attention-level positional encodings such as Rotary PE.\n\nThe authors provide a thorough theoretical analysis, proving that existing methods violate the conservation property and exhibit low faithfulness in tasks that depend heavily on positional information. In contrast, the proposed PA-LRP satisfies the conservation principle and enables more faithful attribution.\n\nExtensive experiments across vision and NLP tasks -- including evaluations on DeiT and LLaMA models -- demonstrate that PA-LRP outperforms baseline methods in most settings.", "questions": "1. In Figure 3, the explainability heatmaps generated by PA-LRP and AttnLRP appear visually similar. Could you elaborate on the qualitative differences that PA-LRP captures and explain why these differences might not be visually apparent despite the claimed improvements?\n\n2. For the perturbation and segmentation tests (Tables 1–3), how many samples were included in the evaluation? Were the results obtained from the full test set or a selected subset? Did you run multiple trials, and if so, could you report any measures of variance or statistical significance?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The authors identify a critical yet previously overlooked limitation in existing LRP-based explainability methods—the lack of attribution to positional encodings, which are essential to Transformer models.\n\n2. The paper introduces new LRP propagation rules tailored for Learnable, Sinusoidal, and Rotary positional encodings, and interprets positional information as a vector passed through attention blocks via a semi-skip connection mechanism, enabling more faithful propagation.\n\n3. The authors provide formal theoretical justifications showing that prior methods violate the conservation property, and prove that the proposed PA-LRP rules satisfy it, ensuring consistency and faithfulness in attribution.\n\nWeakness:\n1. The qualitative examples in Figure 3 do not clearly demonstrate the superiority of PA-LRP over the AttnLRP baseline. The visualizations appear very similar, which weakens the visual impact of the proposed method.\n\n2. Although Tables 1, 2, and 3 report improved quantitative results, the experimental setup lacks detail. In particular, it is unclear how many samples were used in the evaluation, how they were selected, and whether any variance or statistical significance was assessed. The absence of error bars or multiple runs raises concerns about the robustness of the reported improvements.\n\nMinor:\nThe term \"multi-sink mechanism\" used in Table 4 is not explicitly introduced or defined in the main text. While it appears to refer to the layer-wise aggregation of positional relevance across attention layers (as described in Section 3.1 and 3.3), clarifying this terminology when the concept is first introduced would improve readability and help readers better interpret the ablation results. A brief explanation or subsection referencing this mechanism explicitly would be helpful.", "was_revised": true, "final_justification": "I've read the authors' rebuttal, and they have fully addressed my concern regarding Q2 and partially addressed my concern regarding Q1. I will keep my score.", "rebuttal": "Thank you for the comprehensive review and the constructive feedback.\n\n\n>W1. The qualitative examples in Figure 3 do not clearly demonstrate the superiority of PA-LRP over the AttnLRP baseline. The visualizations appear very similar, which weakens the visual impact of the proposed method.\n\nOur most important result is on attention-level PE, such as Rotary PE (RoPE). In this setting, the PE is integrated at each attention layer, making it significantly more impactful in both the forward and backward passes. In this regime, our empirical results are substantially better, as shown by the large performance gap between our method and AttnLRP in Tables 3 and 5, and by the qualitative examples in Figure 7 (for example, note the blue distracting words attributed by AttnLRP, such as “West,” “Harry,” and “Reyn”). Additionally, our theoretical findings are more relevant in this setting. For example, Theorem 3.3 proves that previously proposed LRP methods are not faithful when positional features are involved. It is also important to highlight that attention-level PEs are widely popular in both LLMs (Qwen, LLama, Pythia and others) and vision models like SAM2 (see line 108 in our paper). \n\n>W2 & Q2. Although Tables 1, 2, and 3 report improved quantitative results, the experimental setup lacks detail. In particular, it is unclear how many samples were used in the evaluation, how they were selected, and whether any variance or statistical significance was assessed. The absence of error bars or multiple runs raises concerns about the robustness of the reported improvements…. Q2. For the perturbation and segmentation tests (Tables 1–3), how many samples were included in the evaluation? Were the results obtained from the full test set or a selected subset? Did you run multiple trials, and if so, could you report any measures of variance or statistical signifiance?\n\n\nWe thank the reviewer for the suggestion regarding statistical significance, which we address with dedicated experiments. To ensure the robustness of our results, we report variance across runs and conduct paired t-tests to support statistical significance. The results will appear in the camera-ready version, and we add them to all of the relevant experiments (Tables 1-5). Please see Tables A and B below for perturbation tests in NLP. We demonstrate that the variance is consistently low, and paired t-tests yield small p-values with t-scores aligned with performance trends.\n\n**Table A:** Table 3 from our paper (perturbations in NLP under the finetuning regime), with added statistical information. The P-values are obtained using paired t-tests compared to our method. \n\n\n| Model           | Method  | AUAC ↑ (Gen)                  | AU-MSE ↓ (Gen)                  | AUAC ↑ (Prune)                | AU-MSE ↓ (Prune)                |\n| --------------- | ------- | ----------------------------- | ------------------------------- | ----------------------------- | ------------------------------- |\n| LLaMa-2 7B      | IG      | 0.556 ± 0.03 (P:0.00,T:24.28) | 24.473 ± 0.30 (P:0.00,T:-37.49) | 0.556 ± 0.03 (P:0.00,T:24.46) | 24.438 ± 0.30 (P:0.00,T:24.46)  |\n| LLaMa-2 7B      | Slalom  | 0.606 ± 0.03 (P:0.00,T:20.91) | 18.375 ± 0.28 (P:0.00,T:-28.64) | 0.636 ± 0.03 (P:0.00,T:17.82) | 7.315 ± 0.27 (P:0.00,T:-27.63)  |\n| LLaMa-2 7B      | AttnLRP | 0.779 ± 0.05 (P:0.02,T:2.29)  | 7.629 ± 0.28 (P:0.00,T:-13.31)  | 0.777 ± 0.05 (P:0.00,T:6.47)  | 6.548 ± 0.30 (P:0.00,T:-12.18)  |\n| LLaMa-2 7B      | PE Only | 0.771 ± 0.03                  | 6.792 ± 0.20                    | 0.771 ± 0.03                  | 6.823 ± 0.20                    |\n| LLaMa-2 7B      | Ours    | **0.796 ± 0.03**              | **6.521 ± 0.19**                | **0.790 ± 0.03**              | **6.325 ± 0.19**                |\n|||||||\n| Tiny-LLaMa-2 7B | IG      | 0.637 ± 0.03 (P:0.00,T:29.25) | 13.745 ± 0.24 (P:0.00,T:-33.88) | 0.636 ± 0.03 (P:0.00,T:29.98) | 13.770 ± 0.24 (P:0.00,T:-34.54) |\n| Tiny-LLaMa-2 7B | Slalom  | 0.611 ± 0.03 (P:0.00,T:22.03) | 15.408 ± 0.26 (P:0.00,T:-30.86) | 0.608 ± 0.03 (P:0.00,T:20.67) | 15.666 ± 0.26 (P:0.00,T:-31.20) |\n| Tiny-LLaMa-2 7B | AttnLRP | 0.803 ± 0.05 (P:0.00,T:0.57)  | 8.065 ± 0.26 (P:0.00,T:-29.47)  | 0.792 ± 0.05 (P:0.01,T:2.49)  | 4.030 ± 0.22 (P:0.00,T:1.1)     |\n| Tiny-LLaMa-2 7B | PE Only | 0.788 ± 0.03                  | **3.918 ± 0.16**                | 0.788 ± 0.03                  | **3.947 ± 0.16**                |\n| Tiny-LLaMa-2 7B | Ours    | **0.806 ± 0.03**              | 4.915 ± 0.15               | **0.805 ± 0.15**              | 4.082 ± 0.05                    |\n\n\n___\n\n**Table B:** Table 5 from our paper (perturbations in NLP in zero-shot settings), with added statistical information. \n\n| Method   | G. AUAC ↑           | G. AU-MSE ↓          | P. AUAC ↑           | P. AU-MSE ↓         | G. AUAC ↑           | G. AU-MSE ↓          | P. AUAC ↑           | P. AU-MSE ↓         |\n|----------|---------------------|----------------------|---------------------|---------------------|---------------------|----------------------|---------------------|---------------------|\n|          | **MCQA**  |    **MCQA**  | **MCQA** | **MCQA** | **Token Prediction** | **Token Prediction** | **Token Prediction** | **Token Prediction** |\n| SHAP     | 0.291 ± 0.04        | 141.757 ± 0.71       | 0.282 ± 0.04        | 147.229 ± 0.71      | -                   | -                    | -                   | -                   |\n| IG       | 0.351 ± 0.02 (P:0.00,T:4.46) | 119.984 ± 0.50 (P:0.00,T:-64.09) | 0.339 ± 0.02 (P:0.00,T:4.22) | 123.212 ± 0.50 (P:0.00,T:-63.63) | 0.481 ± 0.04 (P:0.00,T:16.45) | 40.681 ± 0.59 (P:0.78,T:-0.05) | 0.481 ± 0.04 (P:0.00,T:16.46) | 40.750 ± 0.58 (P:0.96,T:-0.05) |\n| AttnLRP  | 0.365 ± 0.04 (P:0.00,T:4.84) | 66.399 ± 0.56 (P:0.00,T:-9.79) | 0.354 ± 0.04 (P:0.00,T:5.43) | 68.856 ± 0.57 (P:0.00,T:-10.33) | 0.559 ± 0.04 (P:0.06,T:1.90) | 41.704 ± 0.58 (P:0.00,T:-21.24) | 0.559 ± 0.04 (P:0.06,T:1.87) | 42.003 ± 0.58 (P:0.00,T:-21.47) |\n| PE Only  | 0.374 ± 0.04 (P:0.01,T:2.51) | **61.014 ± 0.47** (P:0.01,T:2.60) | 0.364 ± 0.04 (P:0.01,T:2.69) | **63.141 ± 0.47** (P:0.00,T:2.85) | 0.557 ± 0.04 (P:0.00,T:11.74) | 40.538 ± 0.58 (P:0.00,T:-3.30) | 0.556 ± 0.04 (P:0.00,T:11.27) | 40.800 ± 0.58 (P:0.00,T:-3.20) |\n| Ours     | **0.377 ± 0.04**    | 61.285 ± 0.47        | **0.368 ± 0.04**    | 63.424 ± 0.47       | **0.562 ± 0.58**    | **40.474 ± 0.34**    | **0.561 ± 0.04**    | **40.735 ± 0.58**   |\n\n\nRegarding the experimental setup, please note that the XAI experiments are deterministic, so the seed has no impact. This methodology was also not employed in previous work [1–3], including papers accepted to ICML and CVPR. Regarding the number of examples, we thank the reviewer for this comment. The information was mistakenly removed from the paper before submission. We will add the number of examples used to each table. For better clarity, we report the number of examples used in each experiment here: For ARC-Easy, we use 5,200 samples; for Wiki-text, 5,000 examples; and for IMDB, 5,000 examples.\n\n\n>Q1 In Figure 3, the explainability heatmaps generated by PA-LRP and AttnLRP appear visually similar. Could you elaborate on the qualitative differences that PA-LRP captures and explain why these differences might not be visually apparent despite the claimed improvements?\n\n\nWe want to clarify that the impact of PE on attribution is less significant for input-level PE (as used in ViT) than for attention-level PE (primarily employed in LLMs). Therefore, the differences are expected to be relatively minor in ViT. That said, we believe Figure 3 (see analysis in lines 302–323) clearly describes the differences between the explainability heatmaps. For example, see line 310:\n\n>> The attributed signal derived solely from positional-associated relevance captures unique relationships, exhibiting clearer spatial and structural patterns. In particular, relevance is distributed across the entire object, especially in the snake, bird, and shark examples. In contrast, the baseline method, which does not propagate relevance through PEs, produces a sparser pattern that does not focus on the entire object but instead is highly selective to specific patches. One possible explanation is that positional-associated relevance better captures concepts related to position, structure, order, and 321 broader regions within the image."}]

Author rebuttal:

Meta-review: This paper proposes an extension of AttnLRP, one of the best performing explanation methods for transformer models. The extension consists of introducing explicit positional embeddings to input and proposing dedicated rules to explicitly assign importance to these embeddings. This extension is theoretically well-motivated (e.g., increase the conservativity and fidelity of the approach) and results in good explanations. The method is extensively evaluated on different datasets and with ablation studies.
The strength of the paper is that it contributes a novel and important insight, namely the importance of taking positional embeddings into account when explaining predictions of transformer models. This insight is theoretically well-motivated and results in good explanations. Experiments are convincing, spanning two domains and sota models in realistic settings. On the negative side, reviewers criticize the narrow focus on LRP and the inconclusiveness of the experimental results (e.g., suggestion for human-based evaluation).
Some of the issues raised by the reviewers could be resolved in the rebuttal. Overall, the reviewers are on average slightly more on the positive side, stressing the importance and potential large impact of the key message of the paper (i.e., role of positional encodings). Since AttnLRP is a state-of-the-art approach for explaining transformers and this topic is of high relevance, even a small improvement (here it also comes with a theoretical motivation) is valuable. Therefore, I recommend to accept the paper.
Decision: Accept (poster)