SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Authors: Shuo Yang
Bardh Prenkaj
Gjergji Kasneci
Venue: ICML 2025 poster (2025)
Keywords: Semantic Bias
Shortcut Learning
Siamese Networks
Model Generalization
Debiasing
Primary area: social_aspects->robustness
TL;DR: SCISSOR is a Siamese network-based debiasing method that mitigates semantic cluster imbalances, improving model robustness and generalization without data augmentation or rewriting.

Abstract: Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we show that imbalances in the semantic distribution of sample embeddings induce spurious semantic correlations, compromising model robustness. To address this issue, we propose SCISSOR (Semantic Cluster Intervention for Suppressing ShORtcut), a Siamese network-based debiasing approach that remaps the semantic space by discouraging latent clusters exploited as shortcuts. Unlike prior data-debiasing approaches, SCISSOR eliminates the need for data augmentation and rewriting. 
We evaluate SCISSOR on 6 models across 4 benchmarks: Chest-XRay and Not-MNIST in computer vision, and GYAFC and Yelp in NLP tasks. Compared to several baselines, SCISSOR reports +5.3 absolute points in F1 score on GYAFC, +7.3 on Yelp, +7.7 on Chest-XRay, and +1 on Not-MNIST. SCISSOR is also highly advantageous for lightweight models with ∼9.5% improvement on F1 for ViT on computer vision datasets and ∼11.9% for BERT on NLP. Our study redefines the landscape of model generalization by addressing overlooked semantic biases, establishing SCISSOR as a foundational framework for mitigating shortcut learning and fostering more robust, bias-resistant AI systems.

SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Shuo Yang! Bardh Prenkaj
Gjergji Kasneci
Abstract
Poctive sample
Furniture
Negative
Shorteut learning undermines model generaliza- tion out-of-distribution data. While the liter- ature attributes shorteuts to biases in superficial features, we show that imbalances in the semantic distribution of sample embeddings induce spuri- ous semantic correlations, compromising model robustness. To address this issue, we propose SCISSOR (Semantic Cluster Intervention for Sup- pressing ShORtcut), a Siamese network-bass based de- biasing approach that remaps the semantic space by discouraging latent clusters exploited as short- cuts. Unlike prior data-debiasing approaches, SCISSOR eliminates the need for data augmen- tation and rewriting. We evaluate SCISSOR on models across 4 benchmarks: Chest-XRay and Not-MNIST in computer vision, and GYAFC and Yelp in NLP tasks. Compared several base- lines, SCISSOR reports +5.3 absolute points in Fl score on GYAFC, +7.3 on Yelp, +7.7 on Chest- XRay, and +1 on Not-MNIST. SCISSOR is also highly advantageous for lightweight models with 99.5% improvement on F1 for ViT on computer vision datasets and ~~1 11.9% for BERT on NLP. Our study redefines the landscape of model gen- eralization by addressing overlooked semantic biases, establishing SCISSOR as foundational framework for mitigating shortcut learning and fostering more robust, bias-resistant AI systems.
Bed
Desk
Coffee Machine
Clacafication hyperphine inflcccaal by shoricsl Clasiiiaatin lyperplase of grond inuth
Safa
Factor Furnituee
Triin
SCISSOR
SCISSOR
Train set
Food
Doughnut
Dread
Cream
Scup
"Foto""Food" "Food"
Figure I. Illustration of sentiment classification, where semantic space shows clusters for "Food" and "Furniture." " "Coffee Machine" and "Ice Cream" are misclassified test samples.
large language models extremely limit their scalability and practical deployment (Li & Liang, 2021; Hu et al., 2022). In contrast, the pre- pre-training and fine-tuning pipeline offers a cost-effective and adaptable solution (Devlin et al., 2019).
However, pre-trained models often fail to maintain the per- formance observed during fine-tuning when applied to re- alistic data (Sun et al., 2024). Further analysis originated this to data biases (Yuan et al., 2024), which is known as the shortcut issue. Specifically, models rely on spurious corre- lations between features and labels, obtaining substantially better results on siieeeendemll-and-ddeeeeed ientddddddnniclllyddiibbbdddddd (ID) data than on nut-of-distribree (OOD) data.
For instance, fact-checking models may evaluate the truth- fulness of a claim by counting its negations (Thorne et al., 2018), and bird models may misclassify birds as waterbirds based on water in the background (Sagawa et al., 2020). Al- though these shortcuts may hold for specific datasets, they significantly hinder the applicability models to roal-world scenarios (Sugawara et al., 2018).
1. Instruction
In recent years, machine learning models have surpassed human capabilities in various domains, such as education and E-commerce (Kasneci al., 2023; Bodonhelyi et al., 2024). However, the high operational and usage costs of
Eqqual contribution 'Technical University of Munich, Munich, Germany. Correspondence to: Gjergji Kasneci <gjorgji.kssecii@mm teu..cc.@tum.e>....
Current research typically attributes shorteuts to the fragility of superficial features, e.g., words or pixels (Chen et al., 2023; Xu et al., 2023a), as opposed to the robustness of se- mantic features. However, we challenge this assumption and argue that the distribution of semantic embeddings (Reimers & Gurevych, 2019) can also appear as shortcuts.
Proceedings of the 4rnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Semantic Cluster Intervention for Suppressing Shortcut
To illustrate this, consider the example in Fig. 1. Suppose train binary sentiment classifier using reviews from biased E-Commerce website, where all food-related reviews are positive, and the term "Ice-cream" is not mentioned. Then, if we apply this classifier to a negative review about Ice-cream, will it classify correctly? It is unlikely, as "Ice- cream" is a food item and its word embedding is likely close to other coodtrelated terms. Consequently. the classifier may associate the semantic region representing food with the "positive" label, thereby losing generalizability. In this case, the shortcut arises not from superficial features, but from semantic information. Similarly, in medicine, semantic shortcuts may lead to the misclassification of conditions in populations with similar physiological characteristics, resulting in fault diagnostics. As for autonomous vehicles, their systems might misinterpret ambiguous road signs due to reliance simplified semantic patterns. All these cases highlight that semantic shortcuts may pose urgent and previously overlooked risk.
cal imaging and hand-written letter recognition tasks.
Our results show that SCISSOR outperforms the base-
lines in terms of accuracy and Fl score.
2. Related Work
Over the past decade, considerable efforts have targeted the challenge of spurious correlations, which often undermine model's OOD performance. Two primary lines of work relevant to our paper focus on: (1) creating balanced and less biased datasets, and (2) counterfactual data genera- tion. In parallel, other broad techniques in distributionally robust optimization (DRO) or group-basee fairness (e.g.. Group DRO, IRM) typically aim to improve worst-case per- formance across predefined demographic groups. However, while these methods are well-suited for known protected attributes, they do not directly tackle latent-space clustering effects, which can give rise to semantic biases that persist even in "balanced" data. Our proposed approach, SCIS- SOR, is instead designed to remap the embedding space itself, complementing both dataset-centric and DRO-based methods by specifically targeting label-skewed clusters.
To address these issues, we propose a novel debiasing ar- chitecture, named SCISSOR (Semantic Cluster Interven- tion for Suppressing ShORtcut), based on a Siamese net- work (Bromley et al., 1993).' Our objective is filter out semantic information irrelevant to downstream tasks from samples exhibiting imbalanced distribution patterns. To achieve this, we first employ the Markov Clustering Algorithm (MCL) (Van Dongen, 2008) to cluster samples based on their semantic similarity, aiming to identify poten- tial imbalanced areas. After that, we construct contrastive data (Chen al., 2020) to train debiasing module, which remaps the semantic space to disrupt the clusters that could act shortcuts, thereby guiding the model to focus on ro- bust features. Different from the triplet loss (Schroff et al., 2015), we consider not only the samples' original labels but also their distribution. Finally, we insert the debiasing module the output of the pre- -rained model and train it jointly with the classification head. Our contributions are as follows:
Creating Balanced and Less Biased Datasets. Several studies aim to address spurious correlations through data manipulation. Wu et al. (2022) propose a data generation strategy for mitigating biases in natural language inference, using a GPT-2 model with unlikelihood training to ensure label consistency and confidence-basee filtering (Bartolo et al., 2021). This process identifies and discards instances that reinforce spurious correlations, thus yielding a more robust dataset. Similarly, Bras et al. (2020) use itera- tive adversarial filtering approach (AFLite; Sakaguchi et al., 2020) to remove highly biased data points. For specific tasks such as fact-checking, CrossAug (Lee et al., 2021) generates negative claims and modifies evidence to create contrastive pairs, improving the model's ability to rely on genuine textual clues. Mcanwhile, CLEVER (Xu al., 2023a) attacks inference-phase biases by subtracting the output of "claim-only" model from more complex fu- sion model. Other techniques include EDA (Wei & Zou, 2019), which relies on random linguistic edits to expand training data, and "Symmetric Test Sets" (Schuster et al., 2019), which eliminates label-specific giveaways in claims. Mahabadi al. (2020) propose "Product of Experts" to downweight spurious signals through a combination of bias-only model with the main classifier. RAZOR (Yang et al., 2024) progressively rewrite training set through word- level feature comparison.
1. Novel conceptualization of semantic bias: To the best of our knowledge, we are the first to identify and demonstrate, both theoretically and empirically, that imbalances in the semantic distribution of samples can also lead to the shortcut problem.
2. Lightweight, plug-and-pp -payy debiasing module: We propose novel debiasing approach that does not aug- ment the training data and operates with the same time complexity as the baseline.
3. Empirical gains across multiple domains: We con- duct experiments on text and image data using six models across text classification, style analysis, medi-
Although these dataset-centric strategies mitigate many surface-level or single-feature biases, they typically entail rewriting, filtering, or augmentation. Such processes depen- dent on careful hyperparameter tuning, and often cannot break deeper correlations within pretrained model's latent
'Code available at https:/ /ttp//gttttu///////////.tt.... con/ ShuoYangtumm SSSR..
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Semantic Cluster Intervention for Suppressing Shortcut
space. In contrast, our approach is module-bused, directly targeting the geometry of the embedding space instead of solely relying on rewriting data. This sidesteps heavy data manipulations or repeated training on LLMs.
show, explicitly addressing these latent clusters substantially boosts dittr--distributionn performance across tasks in both NLP and computer vision.
Counterfactual Data Generation. Another prevalent strat- egy is to produce counterfactual examples - perturbations of existing data designed to disentangle superficial cues from true task-relevant features. Kaushik & Lipton (2018) use manually crafted counterfactuals to demonstrate sig- nificant performance gains on challenging generalization. However, human generation can be time-consuming and often lacks diversity. Automated solutions like Polyjuice (Wu et al., 2021) and Tailor (Ross et al., 2022) fine-tune text generation models to produce specific perturbation types. While powerful, these approaches typically require model retraining when introducing new perturbation classes.
3. Methodology
3.1. Problem Formulation
Let D = [ddi...., d be a dataset containing n samples and its corresponding label yi E V, where ) is the label set. To classify a sample d, first transform it using a specific pre-trained embedding function g DD + X S E. Subsequently, we train classifier fo X + 3 trained by optimizing a specific loss function 0* <- arg ming L(D, 0). Additionally, let us consider another given dataset DD' = {d'...., ] with m samples drawn from a different distri- bution, while sharing the same label set V Let us assume that the samples in D exhibit localized clusters with imbal- anced label distributions when embedded onto the X space. Conversely, the distribution of samples in D' is relatively balanced within the same embedding space.
More recently, DISCO (Chen et al., 2023) leverages large language models to generate candidate phrasal edits, then uses a teacher model to filter out the low-quality ones. Xu et al. (2023b) adopt a similar paradigm for fact verifica- tion, generating tailored counterfactuals that reveal spurious correlations. A employs LLMs to generate counterfactual data to balance the concept of textual data. However, these approaches rely entirely on the usage of LLMs, which can be extremely resource-ing resource-intensive.
Considering these asusmptions, our objectives are twofold:
(1) To demonstrate the existence of semantic bias. We expect that fo trained on X' to achieve better accuracy on a test set drawn from the distribution of X compared to one drawn from X'. Conversely, expect fe, trained on X' to exhibit comparable performance on the test sets drawn from both X, and X"
While counterfactual augmentation can reduce reliance on obvious biases, it does not necessarily eliminate spurious semantic clusters in the embedding space. Even datasets balanced via counterfactual rewriting may contain sub- populations with label skew when projected into latent space particularly if the pretrained model already encodes cer- tain semantically entangled regions. Contrarily, SCISSOR directly "pulls apart" or remaps such clusters via a Siamese network head, forcing the model to focus on truly discrimi- native features rather than latent cluster membership.
(2) To enhance the performance of fa trained on X' on test sets drawn from X" through semantic debiasing algorithms.
3.2. Demonstration of Semantic Bias Existence
To elucidate the underlying causes and influential factors of semantic shortcuts, we propose the following lemmas. Lemma 3.1 quantifies how small changes in the represen- tation space influence the classifier's 's outputs, revealing the extent to which the model's sensitivity to input variations may contribute to semantic bias.
Why SCISSOR? Computational and Conceptual Advan- tages. DRO methods also aim to ensure robustness but typically require explicit group labels or assumptions about how data sub-populations manifest. In real-world settings where biased sub-populations remain hidden or evolve over time, grsup-based constraints may not suffice. SCISSOR circumvents such requirements by first detecting and la- beling suspicious clusters through a lightweight Markov Clustering procedure, then disrupting them. Additionally, SCISSOR maintains low overhead relative to repeated data rewriting or large-scale augmentation, since it slots a debiasing module directly onto a frozen pretrained model, preserving the overall time complexity of forward passes.
Lemma 3.1. Given a differentiable classifier fe. two input samples d, d' E DD function y : D - R2, if the Euclidean distance between g(d) and g(d") in a u-dimensional space is lower than a, then the Euclidean distance between their outputs given from fo is upper- bounded by
1 Vd a [V + Ma,
(I)
where M is the upper bound of the norm of the Hessian matrix of fo over the segment connecting g(d) with gdd").
Hence, our method is complementary to both data-centric and DRO-style approaches, offering novel focus on se- mantic clusters that underlie shortcut learning. As we will
Lemma 3.2 formalizes how local imbalances in training la- bels lead to biased classifier behavior, showing that majority- label dominance causes systematic misclassification of
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Semantic Cluster Intervention for Suppressing Shortcut
minority-labeled samples, with this effect worsening as training progresses.
3.3. Markov Clustering
To determine the initial distribution of the given samples D. we apply the MCL to their embeddings X, which includes three steps.
Lemma 3.2. Let fe be a differentiable classifier trained on data embedded into X S R by a function g. Fix an anchor point X and a radius a > 0. Suppose that within the ball a)e,a) {I E X ||z - cll < a), the training labels are imbalanced-i.e. one majority label is heavily represented compared to a minority label. Then:
Constructing the Markov matrix. We compute the cosine similarity between the embeddings to form similarity ma- trix, which is then normalized to generate a Markov ma- trix (Kelly, 1981). Each entry in the matrix represents the transition probabilities between two samples.
(1) any classifier that minimizes empirical risk will tend to predict the majority label for most points in B(e, oi);
Expansion and inflation. We square the matrix to simu- late the probability distribution after random walks, prop- agating and expanding the connections between samples. Subsequently, we raise each matrix element to its power and normalize again to emphasize stronger connections and suppress weaker ones.
(2) the expected misclassffccation probability on the minority-labeled samples in B(c, o)) is bounded below by positive constant;
(3) the bound increases over the course of training.
Convergence and clustering. By repeating the expansion and inflation, the matrix will converge into a sparse block- diagonal structure, where each block represents a cluster. Finally, we assign samples to different clusters accordingly. For each cluster, we categorize it into two groups based on the label distribution of its samples: i.e., balanced and imbalanced clusters. As discussed in Lemma 3.2, the seman- tics of samples within imbalanced clusters can introduce shortcut learning, hence reducing model generalizability. Therefore, we argue that samples from imbalanced clusters form X and those from balanced ones form the X', Our goal is to mitigate semantic biases present in X to enhance the performance of fe on X'.
We refer the reader to Appendix B for the omitted proof', and to Appendix C for detailed discussion about the sig- nificance of our theoretical findings in semantic biases.
3.2.1. THOORY-GROUNDED OBSERVATIONS
Ennulannc-Induced Misclassification. As a classifier be- comes increasingly accurate on the majority-labeke sam- ples (e.g.. "positives") in a given region, the lower bound on misclassification for minority-labele samples (e.g.. "nega- tives") in the same region grows. In other words, once the model effectively learns to recognize the majority label, any semantically similar minority samples become more likely to be wrongly classified as majority
Additionally, we account for the unequal number of samples within each cluster group. To prevent potential biases caused by sample imbalance, we perform random downsampling such that the number of samples in the two cluster groups is equal, and both groups maintain a balanced label distribution in total. In Appendix E, we discuss on the scalability and time complexity of the MCL.
Concentration Boosts Shorteut Misclassification. As a + O the data around anchor point becomes more tightly concentrated. Under label imbalance, this yields a higher lower bound on the expected misclassification rate for the minority label. In other words, the more localized and imbalanced the samples are, the more the classifier relies on a shortcut that favors the majority label-making minority misclassification increasingly inevitable.
3.4. Semantic Debiasing
We propose a plug-and-plas debiasing module designed to filter out classfication-irreleenn semantic features. We train and integrate a lightweight neural network to the out- put of a pre-trained language model (PLM), remapping its embedding space (Devlin et al., 2019).
Training Exacerbates Shortcut Misclassification. As model parameters converge during training, the lower bound on the expected misclassification probability for sinority-labbled samples rises. This indicates that shortcut-based misclassifi- cations intensify over the course of training, further harming minority classes.
Construction of contrastive data. Here, we present a novel idea for constructing contrastive samples with consid- cration of their semantic distribution. Unlike conventional approaches that solely maximize the distance between sam- ples with different labels (Shah et al., 2022), we disrupt the clustering tendencies within the samples that can serve as shortcuts. Therefore, we introduce the concept of an "inter- mediate sample." For a given anchor, positive samples share
Larger Models Reduce Shortcut Misclassification. If the network's capacity increases reffeeteed by a higher Hessian norm bound M or larger embedding dimension w whehe theoretical lower bound on the expected misclassification probability for minority samples decreases. Consequently, deeper or more expressive models are more resilient against shortcut-basee based misclassification than smaller models.
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Semantic Cluster Intervention for Suppressing Shortcut
Step Clustering and ereate quadrupict.
Step Creating Triplets and train debiasing module.
Step Freeze the debiasing modulc and train classification head.
Quadruplet
A
P
N
Cluster
Combination
Intra-cluster
Anchor
Triplets
PLM
Inter-cluster
* Intermediate
Debiasing
Siamese architecture
Module
Cluster
Positive
PLM S
PLM #
PL.M +
Clussification
Debinsing Module
Debiasing Module
Debiasing Module
Head
Negative
Triplet
LCLS
Figure 2. Overview of SCISSOR. The "Sun" and "Moon" symbols represent samples with two different labels. We first apply the clustering algorithm to group samples hased on the similarity their embeddings, identifying clusters with label imbalances. Next, we generate triplets (Positive, Intermediate, Negative) by selecting anchor sample based on their semantic clustering properties. Their purpose is to guide (train) the debiasing module in remapping the embedding space of the PLM by discouraging shortcut-induces cluster formations, ultimately improving classifier robustness.
the same label but belong to different clusters. Intermedi- samples share the same label and cluster as the anchor. Negative samples have a different label. The intermediate sample plays dual role: (1) it acts as a negative sample when contrasted with positive one, and (2) as positive sample when contrasted with negative one. Any anchor in forms a training quadruplet with its corresponding positive, intermediate and negative samples.
shorteuts from classification iessification-rrelevaan features. We then rely the triplet loss to train the debiasing module.
C Σ max(O. P.) cos(a;, n.) + 8), (2) 1=1
where o,, and nı represent the anchor, positive, and neg- ative samples in the i-th triplet, cos(..)) denotes the cosine distance function, and /3 is the margin ensuring the dis- tance between the anchor and the negative sample exceeds that between the anchor and the positive sample. In this setup. all three samples are simultancously fed into shared- parameter Siamese network, where the goal is to maximize the distance between 0; and n, while minimizing that be- tween a; and p;-
Table Four ways to create triplets from quadruptlet. "A" "P", "I" and "N" stand for anchor, positive, intermediate and negative sample a quadruptiet, respectively.
Triplet factor
Anchor
Positive
Negative
A
P
N
Inter-cluster
A
I
N
N
After training, we implement the debiasing module by in- serting it at the output of frozen PLM. This approach offers two key advantages: (1) high adaptability, and (2) low resource consumption. In other words, the debiasing mod- ule can seamlessly integrate with any architecture without modifying the PL.Ms. Moreover, the time complexity of our lightweight debiasing network is linear, ensuring that the entire complexity dominated by the PLM usage. Finally, SCISSOR does not rely on data augmentation (Lee al., 2021) LLM distillation (Yang al., 2024), distinguishing it from other token- and pixel-level debiasing methods.
Intra-cluster
A
P
I
Therefore, for each quadruplet, there are ways to decompose it into triplets of the form (anchor, posi- tive, negative), as shown in Table 1. Specifically, for each anchor, we employ two methods to construct triplet.
(1) 4 possible
(1) Inter-cluster Contrast. Divide positive and negative sam- ples in the triplet based their labels, encourage samples with different labels to be further apart in the semantic space.
(2) Intra-cluster Contrast. Define positive and negative sam- ples triplets based on clustering characteristics. This pro- motes differentiation among semantically similar samples within the same class, preventing the model from learning
Alternating Training with Clustering and Remapping. Lastly, we employ an alternating training strategy between clustering and debasing. We argue that, as sample embed- ding distributions update, their cluster assignments change
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Semantic Cluster Intervention for Suppressing Shortcut
dynamically. Samples that move out from their original clusters may inadvertently align with others, forming new imbalanced clusters. To prevent this, we alternately imple- ment the clustering and the debiasing. Fig 2 illustrates our method.
NVIDIA A100 Tensor Core-GPUs. Our debiasing network consists of single Transformer module, which includes attention layer and feedforward layer, with 8 attention heads and 768 neurons per layer. Our classification head is a single linear layer with 768 neurons. The value of B in Equation 2 is 0.2. To simplity training, we construct a quadruplet for each anchor using random sampling.
We train the module until the samples within the imbal- anced cluster no longer exhibit clustering tendencies in the remapped semantic space. To quantify the changes in clus- tering behavior, we employ the Hopkins Statistic. During training, this metric gradually increases and stabilizes near 0.5, indicating the removal of clustering tendencies. Finally, we freeze the parameters of the debiasing module and train a classification head with the cross-entropy loss LCLs-
4.3. Results
Validation of Semantic Shortcuts. To experimentally vali- date the theoretical findings presented, we first compared the robustness of classifiers trained on balanced cluster data and imbalanced cluster data. These differences can be measured by evaluating the classifiers' performance on ID test sets versus OOD test sets. We report the classification accuracy and FI scores in Fig. 3. Given that imbalanced clusters exhibit pronounced shortcut features, we treated them as the optimization target and the balanced clusters as test data representing real-world scenarios in the subsequent experi- ments. To further illustrate the inherent clustering structure of the datasets in the semantic space, we calculated the Hop- kins statistic for these datasets after being embedded by the initial model. Tables 2 and 3 show these statistics for the NLP and CV datasets, respectivelz.
4. Experiments
4.1. Datasets
We evaluate SCISSOR across four classification tasks: letter recognition, medical test, sentiment classification and style analysis. These tasks were conducted on two computer vision datasets and two natural language processing datasets.
Computer Vision (CV). The Not-MNIST a multi-label classification dataset (Bulatov, 2011) contains 19,000 im- ages of hand- written letters. The Chest-XRay -Ray dataset (Ha- gos et al., 2023) contains 5,863 XRay images depicting both healthy lungs (0) and lungs affected by pneumonia (1).
Effectiveness of the Proposed Method. To evaluate the debiasing capability of SCISSOR, we evaluate its gain on accuracy and F1 score over the baseline classifiers. We com- pare SCISSOR against three state-of- ofte-of-thheart debiasing meth- ods: RAZOR (Yang et al., 2024) for NLP tasks, LCC (Liu et al., 2023) for CV tasks and IRM (Arjovsky et al., 2019) for both tasks. Specifically, RAZOR relies on rewriting training data containing potential biases using LLMs, while LC corrects classifier logits to balance the gradient impact of majority and minority groups during training. Tables 4 and 5 illustrate the results.
Natural Language Processing (NLP). The Yelp dataset consists of user reviews (positive and negative) of various businesses on Yelp. We use the version presented in (Dai al., 2019). The Grammarly's 's Yahoo Answers Formality Corpus (GYAFC) (Rao & Tetreault, 2018) is the largest dataset for style transfer, containing 1 110,000 informal and formal sentence pairs.
Note that the shortcuts we identified have not been previ- ously reported in the literature. Thus, no standard adversar- ial datasets are currently available for robustness evaluation. To address this limitation, we adopted the cross-validation method shown in (Yang et al., 2024).
Table 2. Hopkins Statistic of NLP datasets. Low values indicate stronger clustering tendency.
BERT
RoBERTa
LLaMA
4.2. Experimental Setup
GYAFC
1.59x 10-8
5.42x10-8 5.59x10-8
We use six models as baseline classifiers fo: BERT (De- vlin et al., 2019), RoBERTa (Liu et al., 2019), LLaMA 3.2 (Meta, 2024), Vision Transformer (VIT) (Dosovitskiy et al., 2020), Shifted Window Hierarchical Visio nans- former (Swin) (Liu et al., 2021), and DINOv2 (Oquab et al., 2024).² We employ the AdamW optimizer (Loshchilov & Hutter, 2019) with an initial learning rate of 3 x 10-5 and trained the models with batch size equal 8 across four
Yelp 1.74x10-8
3.34x10-4
Table 3. Hopkins Statistic of CV datasets. Low values indicate stronger clustering tendency.
VIT
Swin
DINOv2
Chest-XRay Not-MNIST
8.87x10-1 7.83x10-9 -9 4.93x10-9
5.06 10-8
?All models are publicly available at https: / //huggingface..com
4.07x10-7
3.01x10-9
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Semantic Cluster Intervention for Suppressing Shortcut
AccurRES CLrVES rodben trained bawnced
Accurecy of model trained on bateroed skatters Oharge
Ascuriody of models mranedion belavoed ckaster (Chest-Shay)
Ancunaay ourves reedes ttained belanoed chasters
clasters (SMAFC)
disters (Wep)
diatters (Chedt-SRay)
Cuattrr (icdiNGTT)
Figure 3. Models trained on imbalanced clusters exhibit significant performance drops OOD data, confirming that semantic bias harms generalization, while balanced training improves robustness. Wc show the performance of classifiers on balanced and imbalanced clusters under ID) and OOD test data. For each data group, we randomly choose 500 from the training set to form the test set.
Table 4. SCISSOR's ' impact on the baseline classifiers on the NLP datasets in terms of Accuracy and Fl score. Bold values indicate best performing baseline; underlined the sccond- best.
Table 5. SCISSOR'S 's impact on the baseline classifiers on the CV datasets in terms of Accuracy and Fl score. Bold values indicate the best performing per baseline; underlined the second-best
GYAFC
Yelp
Chest-XRay
Not-MNIST
1CC
FI
ACC f
F1
ACC1 t
F1 1
ACC
F1 1
BERT
70.40
0.70
81.17
0.81
VIT
72.38
0.72
88.87
0.89
BERT (w/ IRM)
77.82
0.77
87.29
0.87
VIT (w/ IRM)
80.47
0.80
89.37
0.89
BERT (w/ RAZOR)
72.76
0.71
82.20
0.82
VIT (W/ LC)
82,73
0.82
89.25
0.89
BERT (w/ SCISSOR)
78.20
0.78
90.65
0.91
VIT (W/ SCISSOR)
83.92
0.84
90.89
0.91
RoBERTa
73.66
0.73
75.76
0.76
Swin
84.54
0.84
92.72
0.93
RoBERTa (w/ IRM)
79.61
0.79
84.53
0.84
Swin (W/ IRM)
76.92
0.76
92.12
0.92
RoBERTa (w/ RAZOR)
73.58
0.73
76.36
0.76
Swin (w/ LC)
79.75
0.79
91.56
0.92
RoBERTa (w/ SCISSOR)
81,34
0.81
87.79
0.88
Swin (w/ SCISSOR)
88.65
0.89
92.74
0.93
LLaMA
89.40
0.89
95.00
0.95
DINOv2
68.94
0.66
85.40
0.85
LLaMA (w/ IRM)
83.65
0.83
94.87
0.95
DINOv2 (w/ IRM)
69.64
0.67
85.39
0.85
LLaMA (w/ RAZOR)
87.00
0.86
94.44
0.94
DINOv2 (w/ LC)
72.73
0.72
83.63
0.84
LLaMA (w/ SCISSOR)
89.46
0.89
95.20
0.95
DINOv2 (w/ SCISSOR)
73.59
0.72
85.75
0.86
Table 6. Adjusted rand index between topics and semantic clusters.
4.4. Analysis and Discussion
BERT
RoBERTa
LLaMA
Larger models exhibit up x10 higher Hopkins statis- tics, revealing a weaker cluster effect in embeddings. From the Hopkins statistics reported in Tables 2 and 3, we observe that all models produce values close to 0 across every dataset. Nonetheless, Yelp and GYAFC share rela- tively similar clustering tendencies, whereas Not-MNIST exhibits markedly higher randomness compared to Chest- XRay. This finding suggests that natural data tends to exhibit a strong inherent clustering structure within the embedding space of pretrained language models (PLMs). Such obser- vation provides theoretical support for SCISSOR's ' approach of assigning cluster-based labels in this space.
GYAFC
0.62
0.13
0.00
Yelp
1.93
2.44
0.15
dataset, while larger models like LLaMA display less con- centration. In vision models, VIT and DINOv2 similarly show Hopkins statistics that are one to two orders of magni- tude higher than Swin, consistent with the trend that larger parameter counts lead to higher Hopkins values.
Semantic Imbalance Causes Up to 20-Point OOD Ac- curacy Drops, While Balanced Training Enhances Ro- bustness. Under in-distribution (ID) conditions, all models achieve high accuracy (Fig. 3). However, when trained on semantically imbalanced datasets, their performance sub- stantially degrades on out-of-distribute (OOD) test sets.
Additionally, we note that the Hopkins statistic monoton- ically increases with model size. Smaller networks yield more concentrated embedding distributions for the same
7

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Semantic Cluster Intervention for Suppressing Shortcut
Table 7. Ablation study on the NI.P datasets in terms of Accuracy and F1 with K-means clustering.
GYAFC
Yelp
ACC1 f
F1
ACC f
F1 t
BERT
70.40
0.70
81.17
0.81
BERT (w/ RAZOR)
72.76
0.71
82.20
0.82
BERT (W/ Triplet)
77.60
0.77
88.60
0.89
BERT (w/ SCISSOR, k-means)
76.52
0.75
88.50
0.89
BERT (w/ SCISSOR)
78.20
0.78
90.65
0.91
RoBERTa
73.66
0.73
75.76
0.76
RoBERTa (w/ RAZOR)
73.58
0.73
76.36
0.76
RoBERTa (w/ Triplet)
81.32
0.81
87.70
0.87
RoBERTa (w/ SCISSOR, k-means)
81.30
0.81
87.78
0.87
RoBERTa (w/ SCISSOR)
81.34
0.81
87.79
0.88
LLaMA
89.40
0.89
95.00
0.95
LLaMA (w/ RAZOR)
87.00
0.86
94.44
0.94
LLaMA (w/ Triplet)
89.40
0.89
94.64
0.94
LLaMA (w/ SCISSOR, k-means)
89.42
0.89
94.90
0.95
LLaMA (w/ SCISSOR)
89.46
0.89
95.20
0.95
This underscores a robustness gap driven by shortcut learn- ing. In computer vision (CV), the largest OOD accuracy drop occurs on Chest-XRay-Ba ViT and DINOv2 each lose 20 points, and even Swin experiences a 2-point decline on Not-MNIST. By contrast, when training data exhibits balanced semantic clusters, there is consistent ID-OOD performance gap, indicating a higher degree of robustness.
LLaMA's accuracy by ~2 points.
In computer vision, SCISSOR's largest gains occur on Chest-XRay, where ViT achieves 12-point increase in both accuracy and F1 on the OOD set. Across the board, SCISSOR consistently outperforms LC in terms of both ac- curacy and F1. We attribute LC's shortfall to its inability to address deeper semantic biases in balanced-label scenarios, thereby limiting its improvement potential. Performance improvements on Not-MNIST are relatively smaller, likely due to the dataset's weaker embedding clusters and lower susceptibility to shorteut issues; even so, SCISSOR still provides 2-point lift in accuracy and Fl for ViT.
similar pattern emerges in textual datasets, with the short- cut effect most pronounced on GYAFC, Among the lan- guage models tested, BERT -ERTnwhen trained on imbalanced clusters-shows the greatest performance gap 20 points) between ID and OOD. However, when BERT is trained on balanced data, the gap narrows 3 points. Notably, LLaMA achieves nearly identical results on both ID and OOD tests. As shown in Table 2, LLaMA exhibits strong overall performance and lower embedding concentration, making it less prone shorteut-driven errors.
We observe that although IRM mitigatesrtcutss in many cases, our method still significantly outperforms it across all tests. Moreover, IRM performs worse than the baseline on small datasets, such as Chest-XRe (w/ SWIN) and GYAFC (w/ LLaMA), We attribute this IRM assigning excessive train- ing weight features that remain invariant, which prevents other useful features from being accurately identified and utilized.
SCISSOR Achieves Up to 12-Point Gains in Accuracy and F1 Across NLP and Vision Tasks. Tables 4 and 5 summarize the performance improvements introduced by SCISSOR. In the NLP domain (Table 4), SCISSOR delivers notable gains for all three language models. On GYAFC, for instance, BERT and RoBERTa each see a 7-point boost in ac- curacy and Fl score relative the RAZOR baseline, while on Yelp, SCISSOR outperforms both BERT and RoBERTa by 9 and 12 points, respectively. Although LLaMA already exhibits robust performance against shortcuts, it still real- izes marginal benefits from SCISSOR. Moreover, because the datasets in question are label-balanced and contain lim- ited superficial shortcuts, RAZOR's strategy of manipulat- ing superficial features and data rewriting actually lowers
4.4.1. WIIY DO SEMANTIC CLUSTERS MATTER?
While our experiments demonstrate that debiasing seman- tic clusters improves generalization, one might still ask: What do these clusters actually represent in practice? To investigate,
hypothesize that samples within the same cluster tend to share common semantic themes or topics. To test this, we trained Latent Dirichlet Allocation (LDA) topic model (Blei et al., 2003) and measured the alignment between semantic clusters and topic clusters using the Ad- justed Rand Index (ARI) (Hubert & Arabie, 1985). The
8

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Semantic Cluster Intervention for Suppressing Shortcut
Table 8. Ahlation study the CV datasets in terms of Accuracy and Fl score.
Chest-XRas XRay
Not-MNIST
ACC 1
F1 f
ACC 1
F1
VIT
72.38
0.72
88.87
0.89
VIT (w/ LC)
82.73
0.82
89.25
0.89
VIT (w/ Triplet)
82.12
0.81
88.97
0.89
VIT (w/ SCISSOR, k-means)
82.20
0.82
89.44
0,90
VIT (w/ SCISSOR)
83.92
0.84
90.89
0.91
Swin
84.54
0.84
92.72
0.93
Swin (w/ LC)
79.75
0.79
91.56
0.92
Swin (w/ Triplet)
86.64
0.86
91.00
0.91
Swin (w/ SCISSOR, k-means)
87.58
0.87
92.72
0.93
Swin (w/ SCISSOR)
88.65
0.89
92.74
0.93
DINOv2
68.94
0.66
85.40
0,85
DINOv2 (w/ LC)
72.73
0.72
83.63
0.84
DINOv2 (w/ Triplet)
72.88
0.72
84.58
0.85
DINOv2 (w/ SCISSOR, k-means)
72.80
0.72
85.70
0.85
DINOv2 (w/ SCISSOR)
73.59
0.72
85.75
0.86
results, shown Table 6, reveal a clear positive correlation between semantic clustering and topic clustering across all datasets and models. This strongly suggests that semantic clusters not just artifacts of model embeddings- they capture meaningful. high-level concepts in the data. An in- triguing insight emerges from our findings: stronger models, such as LLaMA, exhibit significantly lower ARI scores. This implies that as models grow in capacity, their embeddings become less tightly coupled to discrete topics. In other words, more powerful models learn richer, more distributed representations, rather than rigidly grouping samples by surface-level themes. This phenomenon aligns with our earlier observation that larger models naturally more resistant to shortcut learning key insight into why SCIS- SOR has greater impact on smaller architectures. These findings provide compelling evidence that shortcut learn- ing fundamentally tied to how models organize semantic information, and that disrupting these clusters can lead to more robust, generalizable classifiers.
maintaining a significant advantage over the baselines.
Additionally, compared to Triplet, SCISSOR consistently demonstrates advantage about 2 points. We analyze that this is because Triplet focuses solely on optimizing samples based on their classification labels, neglecting the impor- tance of the embedding distribution. During the training process of Triplet, samples with the same label are pulled closer together, which could lead to the formation of new imbalanced semantic clusters.
5. Conclusion
Shortcut learning remains fundamental challenge in ma- chine learning undermining model generalization by en- couraging reliance on spurious correlations. While prior work has focused primarily on surface-level biases, we reveal that semantic clustering effects within embedding spaces also contribute significantly to shortcut-driven fail- ures. To address this, introduce SCISSOR, novel cluster-aware Siamese network that actively disrupts latent semantic shortcuts without requiring data augmentation or rewriting. Our extensive experiments across six models and four benchmarks demonstrate that SCISSOR substan- tially improves out-of-distribution robustness, particularly in settings where traditional debiasing techniques struggle, Notably, SCISSOR achieves +7.7 F1 points on Chest-XRay +7.3 on Yelp, and +5.3 GYAFC, setting new standard for mitigating shortcut learning at the semantic level. Fur- thermore, we show that larger models are naturally more re- sistant to these biases, yet SCISSOR enhances even smaller models, making them competitive with larger, more com- plex architectures.
4.5. Ablation Study
We investigated the impact of clustering algorithms on the effectiveness of SCISSOR. Specifically, we replaced MCL. with the K-means clustering algorithm and repeated the comparative experiments with the baselines, as shown in Table 7 and 8.
We observed that the clustering algorithm cannot signifi- cantly impact the effectiveness of SCISSOR. After replacing MCL with K-means, which holds a linear time complex- ity with respect data scale, our approach showed almost identical performance in Accuracy and Fl scores while

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Semantic Cluster Intervention for Suppressing Shortcut
Impact Statement
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. A simple framework for contrastive learning of visual rep- resentations. In Proceedings of the 37th International Conference on Machine Learning, ICML'20. JMLR.org, 2020.
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Chen, Z., Gao, Q. Bosselut, A., Sabharwal, A., and Richard- son, K. DISCO: Distilling counterfactuals with large language models. In Rogers, A., Boyd-Graber, J., and Okazaki, N. (eds.), Proceedings of the 61st Annual Meet- ing of the Association for Computational Linguisties (Vol- une I: Long Papers), pp. 5514-5528, Toronto, Canada, July 2023. Association for Computational Linguistics. URL. https:
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Yuan, Y., Zhao, L., Zhang, K., Zheng, G., and Liu, Q Do LLMs overcome shortcut learning? evaluation of shorteut challenges in large language models. In Al- Onaizan, Y., Bansal, M., and Chen, Y.-.. (eds.), Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 12188-12200 Miami, Florida, USA, November 2024. Association for Compu- tational Linguisties. doi: 10.18653/v1/2224.cmmm main. 679. URL https: /aacaaaaattth wor...og///00.. emnlp main. 679/.
Wei, J. W. and Zou, K. EDA: easy data augmentation tech- niques for boosting performance on text classification tasks. In Inui, K., Jiang, J., Ng. V., and Wan, X. (eds.), Proceedings of the 2019 Conference on Empirical Meth- ods in Natural Language Processing and the 9th Interna- tional Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp. 6381 -6387. Association for Computational Linguistics, 2019. doi: 10.18653/ 1670. URL https: /doi org/ 10. www.g//.......... www.:/16633vv11 1670.
Wu, T., Ribeiro, M. T., Heer, J., and Weld, D. Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models. In Zong, C., Xia, F., Li, W., and Navigli, R. (eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natu- ral Language Processing (Volume 1: Long Papers), pp- 6707-6723, Online, August 2021. Association for Com- putational Linguistics. doi: 10. 10.186663/122022.alllorz 523. URL https: http://aaaaattoloyyooooooo wrg.://ooorrr//2220/ acl ael-looe 1oll-oog.555 523.
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13

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Semantic Cluster Intervention for Suppressing Shortcut
A. More Theoretical Study
A.1. Why Can SCISSOR Reduce Generalization Error?
Typically, the generalization error is:
(n - Em [L(fo(r))
where D [L(Je(r)))))) the loss on the OOD test set and B.... (L)) the loss on during training.
However, it is challenging to pre-define the distribution patterns of OOD data. Therefore, we use Rademacher Complexity to measure the fitting ability of SCISSOR random noise, which in turn reflects its generalization capability. The Rademacher Complexity is define by:
m2
R₂(F) = Eg sup JEF m
Σ (z,)
(4)
where supyer represents finding the function f within the hypothesis class F that maximizes the Rademacher Complexity. 0; is the Rademacher variable, ranging from [1.
Theoretically, SCISSOR increases the intra-cluster contrastive loss, which filters out semantic information related to clustering. As result, it expands the effective dimensionality the data. This enables the model learn more robust, Mussification-releaaan features rather than relying on unstable semantic shortcuts, thereby reducing its ability fit noise. Consequently, the Rademacher complexity of decreases, indicating stronger generalization ability, which in turn reduces generalization error.
A.2. Why Is SCISSOR's Classification Hyperplane More Robust?
To further illustrate how SCISSOR remap the semantic space structure support the building of the classification boundary. introduce convex geometric analysis. We assume that sample embedding z corresponds specific label within the label set ) = {y1..... yN }or and that samples of class Vi are distributed within the label spherical cluster C. Typically, the classifier performs precise classification by separating these label spherical clusters. However, when x is unevenly distributed, difficult to establish an accurate classification plane.
We assume that the spherical centers of any cluster C; is given by p4:
3 E Eg
(5)
the inter-class separation can be measured using cosine similarity:
cos(0) (0) Eimo EN
2.j0 Tas [ay
(6)
The larger the cos(O), the higher the degree of overlap between the class boundaries, making it difficult to establish an accurate classification plane. We calculated change in cos( denoted as A6, before and after SCISSOR training, i.e. Obetore and Oaterr to demonstrate how SCISSOR increases the distance between samples with different labels:
A = (osetreerree fatter)
The results is shown Table and 10. We observed that by using SCISSOR, the distance between different label samples increased for all models across all datasets.
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Semantic Cluster Intervention for Suppressing Shortcut
Table 9, A0 of NLP datasets. Positive values indicate that distance between samples with different labels increased.
BERT
RoBERTa
LLaMA
GYAFC
0.40
0.40
0.02
Yelp 0.26
0.19
0.01
Table 10. A CV datasets. Positive values indicate that the distance between samples with different labels increased.
VIT
Swin
DINOv2
Chest-XRay
0.35
0.22
0.32
Not-MNIST 0.16
0.05
0.17
B. Omitted Proofs
B.1. Lemma 1
Proof. Given llg(d) 9l2 a, according the Cauchy-Schwarz Schwarz inequality (Garibaldi et al., 2007), the following holds for Ve:
||l||
0
Vu. ||01|2.
(8)
Let = -
g(d)) g(d)), then:
(lg(d) s(d')l S Vu- (l9(d) - g(d')l22 avu.
(9)
Since fe is differentiable, we apply its Taylor expansion (Taylor, 1715) as follows:
folg(d')) folo(d)) Vfolo(d)) g(d')) R.
(10)
where R represents the higher-order terms, satisfying:
R 1 -())))=o g(d)),
(11)
where, fo() is the Hessian matrix (Hemati et al., 2023) of so with & E (b(d).
Assuming that there is no gradient explosion issue in fe, it has a finite number of parameters, and all parameters are bounded, then H is continuous, and its norm has an upper bound M:
[Hfo(S)la S M, e Io(d),
(12)
Here,
1 ||]|| < マ Mla(()) - = Ma.
(13)
Substituting it into Eq. (10), we have:
((o(d)-(((dd)))) ( (((((d)) f((((d))) < <[Vio(((d)) (l(()) s(d')l Ma
(14)
S a (V + Ma
15

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Semantic Cluster Intervention for Suppressing Shortcut
B.2. Lemma 2
Proof. Let us consider a multi-label classification, within the neighborhood of c, given a majority sample set D (di, id with corresponding label Umajor. and any minority sample set D (di,...,di)) dvv.) with corresponding label Yminor- We suppose Ni >> N2, and the output of fe is the probability of sample obtaining label Ymajors that is fo(g(d) P(Mmajor z2; 0). By applying the Lemma 1, we have
P(yle; P(uld:0)l2 (| P(yld: 0)1l
(15)
< a. (Vfo((Vol)()))l Ma,
and
[P(ycc P(yld; 0) - (P((cc Plyld: 0)
(16)
-Vf(ocll Ma.
Therefore, for Ad, Ad, The following inequality holds
[P P(yld: 0)1 2/u [ Ma.
(17)
For the gradient update of fa. we can decompose into the sum of two parts:
aL
0Lx
0Lx
(18)
00
00
00
Since N1 >> N₂
+
1Lx 00
So will prioritize ensuring convergence for X. Therefore, we have P(yld;0)
P(yld; 0).
Substituting it into Eq. (17), we have
P(uld:0) > P(yld; 0) 2/u a (e-IVVooo((e))le - Ma.
(19)
B.3. Proof of Upper Bound for Hessian Matrix (cont. Lemma 1)
Proof. We are going to prove that the spectral norm of the Hessian matrix mentioned in Lemma I has upper bound, i.e., || fo( s M, V (g(d), s(d)) holds, take fully connected network as an example:
Let the classifier fo be an L-layer fully connected neural network with parameters = Wi, bi,., Wr, br. where the input x E ddi and the output is y e k The computation at each layer is defined as follows:
z1 (ia + bi:
(20)
where a is the activation function and 20 = x and the norm of the input x is bounded by B.
Here, popular activation functions such as Sigmoid, Swish, and GLUE are twice differentiable with bounded second derivatives. Furthermore, the weight matrix of each layer satisfies ||Will2 I C, where C: is constant.
For the Hessian matrix H which the k-th component of the output få (z) can be expanded as:
82 f
H
Cross Terms, aw?
(21)
1=1
To simplify the analysis, we focus on the diagonal blocks and ignore the cross terms, as their norms are typically much smaller than those of the main diagonal blocks.
Therefore, for the I-th layer, we have:
02 fb H
Ofb OW,
OW?
OW,
(22)
| < K 1|
|/
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Semantic Cluster Intervention for Suppressing Shortcut
Furthermore, with the condition || C and ||ا|| B, we obtain the following inequality using induction:
1-1
|10-1112 S B.
II
Cillo'I
==1
(23)
|1 K K. B Of P
The spectral norm the Hessian matrix of fa can be obtained by summing the norms of the Hessian blocks from each layer:
| 11
(24)
= *IAAo((E)1a K. B² Σ (C II 1
C. Why Our Theory Makes Sense to Demonstrate Bias Existence?
C.I. Lemma 3.1
This lemma is useful in demonstrating the existence of semantic bias in text and images because it provides mathematical bound on how small changes in the input space (according to some function g) translate into changes in the classifier's output. The key insight is that if two semantically similar inputs (i.e., ones mapped close to each other by g) result in significantly different outputs, it suggests that the classifier is sensitive to specific aspects of the input that may not align with human perception of mnmilarity-indicaan bias.
Why This Lemma Makes Sense Demonstrating Bias.
(1) Local Sensitivity to Features: The lemma highlights how changes input embeddings g(d) and g(d") propagate through the classifier. If the upper bound is large for semantically similar inputs, the classifier may be over-sensitive to subtle, potentially biased variations.
(2) Dependence on Gradient and Curvature: The bound depends on V fo (G(())) and the Hessian norm M, meaning that sharp decision boundaries (high curvature) and large gradient norms can amplify small differences in representation space, possibly leading to biased decisions.
(3) Quantifying Semantic Bias: If semantic similarity (low Ilg(d) g(d')l does not guarantee correspondingly small change in the classifier's output, this suggests that the classifier does not treat semantically similar inputs equivalently- an indication of bias.
C.2. Lemma 3.2
This lemma provides theoretical justification for how local label imbalances lead biased classifier behavior, reinforeing the existence of semantic bias in text and images. It highlights the mechanism by which majority label dominance affects classification decisions within local region of the representation space.
Why This Lemma Makes Sense Demonstrating Bias.
(1) Local Majority Influence: If one label dominates in a local region of the embedding space B(c, a)) empirical risk minimization (ERM) encourages the classifier to favor that majority label, leading to biased predictions.
(2) Persistent Misclassification of the Minority Class: The second claim establishes lower bound on the classifier's misclassification probability for the minority class, demonstrating that bias is not incidental-itt structurally inevitable due to the local label imbalance.
(3) Worsening Over Time: The third claim states that this misclassification bias increases during training, suggesting that deeper training exacerbates bias rather than mitigating it. This aligns with real-world observations where models tend to overfit dominant patterns in the data.
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Semantic Cluster Intervention for Suppressing Shortcut
How This Relates to Semantic Bias.
(I) If classifier systematically favors sngjoritylabelle instances, it implies that certain semantics (c.g., particular words, styles, or features) are overprivileged, while others are suppressed.
(2) This is particularly relevant in applications like natural language processing and computer vision, where spurious correlations (e.g,, associating specific words with certain sentiments or skin tones with particular classifications) arise due to dataset biases.
D. Supplementary Experiments
D.1. Precision-Recal Curve
To further demonstrate the effectiveness of SCISSOR, we plot the precision-recall (PR) curves on the 4 datasets used, as shown in Fig. 4.
We observe that in all cases, SCISSOR consistently shows higher Area Under the Curve (AUC) than the baselines. the CV domain, the largest improvement comes from the ViT and DINOv2 models on the Chest-XRay dataset. As for NLP datasets, the most significant difference is observed with the BERT and RoBERTa models on the Yelp dataset. For LLaMA3, still observe that our PR curve consistently lies above that
the improvement of SCISSOR is relatively slight, whereas of the baseline.
Model
BERT
RoBERTa
LLaMA3
Intra-cluster
Inter-cluster
Intra-cluster
Inter-cluster
Intra-cluster
Inter-Cluster
GYAFC Yelp
3.06
1.87 *
2.53
2.26 *
2.34
2.27 *
11.35
6.80 *
11.56
6.50 *
7.12 *
6.99 *
Table 11. Jaccard coefficient of words. For clear comparison, we increased the value by 100 times.
indicates a statistically significant
difference between the Intra-cluster Inter-cluster groups, with a p-value less than 0.0001.
Model
BERT
RoBERTa
LLaMA3
Intra-cluster
Inter-cluster
Intra-cluster Inter-cluster
cntra-cluster
Hter-Cllster
GYAFC Yelp
25.28
23.38
24.01
23.34 #
23.51
23.53
27.26
24.78 *
27.75
24.78 *
24.98
25.11
Table 12, Levenshtin similarity hetween sequence of words. For clear comparison, we increased the value by 100 times. indicates statistically significant difference between the Intra-cluster and Inter-cluster groups, with p-value less than 0.0001.
D.2. Principal Component Analysis
To visually demonstrate how SCISSOR remaps the semantic space, we selected the RoBERTa model with the greatest performance improvement in the experiment, along with the Yelp dataset, for testing. We applied Principal Component Analysis (PCA) reduce the outputs of both the debiased module and the original RoBERTa outputs dimensions, shown in Figure 5.
D.3. The Relationship Between Semantic Space Clustering and Linguistic Features
As additional part, we propose two hypotheses specifically for the embeddings generated by language models:
Samples within the same cluster may share more similar word compositions. To verify this, we conducted 100,000 random sampling trials and calculated the Jaccard similarity coefficients for intra-cluster and inter-cluster pairs. The results are presented in Table 11.
Samples within the same cluster may exhibit higher word string matching. We performed 100,000 random sampling trials and computed Levenshtein similarity scores to evaluate the differences between randomly selected intra-cluster and inter-cluster pairs. The results are shown in Table 12.
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Semantic Cluster Intervention for Suppressing Shortcut
Chest
Chest XRay
Chest-ilay
2.0
Micdet
Syb
26.67
3.2
0.0
0.3
Bacoal
NORWNIST
NotMNIST
NotWNIST
0.0
0.5
3-2
Model
622
Moddes
82
MSERK
5ym
2.0
0.0
2.2
2.4
0.5
0.8
10
Recal
Recill
Racal
GYACC
CHAFC
GYAFC
2.8
0.0
0.5
0.2
0.2
Node
0.2 Vecam
Oui leffeberto
11.5
100
3.0
611
1.4
0.8
Nocull
1.5
%elp
1.0
Yelp
1.2
Nelp
0.5
02
0.2 Veekh
0.0
0.6
10
Recal
Recil
Figure 4. Precision-Reccd curve.
We observe strong correlation between semantic clusters and both word overlap and string matching, as shown in Tables 11 and 12. Samples within the same cluster (intra-cluster) exhibit significantly higher Jaccard index compared to those in different clusters (inter-cluster). In particular, for embeddings from the BERT model, the Jaccard index for intra-cluster pairs is approximately 70% higher than that for inter-cluster pairs.
Similarly, intra-cluster pairs for BERT and RoBERTa embeddings exhibit significantly higher Levenshtein similarity than inter-cluster pairs. However, for the more powerful model LLaMA, the differences between the two groups are not statistically significant.
19

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Semantic Cluster Intervention for Suppressing Shortcut
PCA of the outputs of Baseline Iw/Rabertal on the Yelp
PCA the outputs SCISSOR (w/Robeerta) on the Yelp
Fostive Negsttve
Postive
Negative
0.10
0.1
0.05
00-
Prenccal
0.00
-9.55
-4.10
-03-
-0.15
-0.4
-02
0.0
0.2
0.6
-2
-1
Pricipal Component 3
Prinipal Component
Figure The comparison PCA results for positive and negative sample embeddings in semantic space SCISSOR( and Roberta, with the experiment conducted on the Yelp training set.
E. Scalability and Complexity of MCL
Although Markov clustering can incur a quadratic cost due to pairwise similarity computations, we underscore that performed offline on the training set-i.e., it does not need be repeated once the clusters are established. In practice, especially for large datasets, one can mitigate the computational load by adopting approximate neighst methods (e.g., FAISS (Johnson et al., 2017) HNSW (Malkov & Yashunin, 2018)) to sparsify the initial similarity graph before running the Markov clustering. This significantly reduces both runtime and memory overhead without compromising the integrity of the clusters. Morcover, advanced parallelization strategies, such as Block-partitionas similarity calculations on GPUs, can further scale our clustering to millions of samples. As a result, while the theoretical complexity is (n²)), wegleengi approximations allow Markov clustering to be applied efficiently in domains like vision and NLP where data can be extensive. Crucially, this one-time clustering does not impact inference-time latency; once the semantic clusters identified, SCISSOR integrates seamlessly as a lightweight module on top of the frozen pretrained model.
20


Reviews:
[{"review_id": "YvrjH1remq", "reviewer": "Reviewer_gyYJ", "summary": "This work introduces SCISSOR (Semantic Cluster Intervention for Suppressing Shortcut), a Siamese network-based debiasing approach that remaps the semantic space by discouraging latent clusters exploited as shortcuts. Shortcut learning is a critical issue that undermines model generalization to out-of-distribution data. Through extensive evaluation on various models and benchmarks, SCISSOR demonstrates its effectiveness in mitigating shortcut learning and promoting more robust machine learning models.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1. We added a comparative experiment with Invariant Risk Minimization (IRM) [1] and will include the results as well the corresponding references in the Related Work section.**\n\n| Dataset        | ACC (GYAFC) | F1 (GYAFC) |  | ACC (Yelp) | F1 (Yelp) |\n|---------------|------------|------------|--|------------|-----------|\n| BERT           | 70.40       | 0.70       |  | 81.17      | 0.81      |\n| BERT (w/IRM)   | 77.82       | 0.77       |  | 87.29      | 0.87      |\n| BERT (w/Ours)   | 78.20       | 0.78       |  | 90.65      | 0.91      |\n| RoBERTa        | 73.66       | 0.73       |  | 75.76      | 0.76      |\n| RoBERTa (w/IRM) | 79.61       | 0.79       |  | 84.53      | 0.84      |\n| RoBERTa (w/Ours) | 81.34       | 0.81       |  | 87.79      | 0.88      |\n| LLaMA          | 89.40       | 0.89       |  | 95.00      | 0.95      |\n| LLaMA (w/IRM)  | 83.65       | 0.83       |  | 94.87      | 0.95      |\n| LLaMA (w/Ours)  | 89.46       | 0.89       |  | 95.20      | 0.95      |\n\n|Dataset|ACC (Chest-XRay)|F1 (Chest-XRay)| |ACC (Not-MNIST)|F1 (Not-MNIST)|\n|---------------|------------|------------|--|------------|-----------|\n| ViT           | 72.38       | 0.72       |  | 88.87      | 0.89      |\n| ViT (w/IRM)   | 80.47       | 0.80       |  | 89.37      | 0.89      |\n| ViT (w/Ours)   | 83.92       | 0.84       |  | 90.89      | 0.91      |\n| SWIN        | 84.54       | 0.84       |  | 92.72      | 0.93      |\n| SWIN (w/IRM) | 76.92       | 0.76       |  | 92.12      | 0.92      |\n| SWIN (w/Ours) | 88.65       | 0.89       |  | 92.74      | 0.93      |\n| DINOv2          | 68.94       | 0.66       |  | 85.40      | 0.85      |\n| DINOv2 (w/IRM)  | 69.64       | 0.67       |  | 85.39      | 0.85      |\n| DINOv2 (w/Ours)  | 73.59       | 0.72       |  | 85.75      | 0.86      |\n\nWe observe that although IRM does mitigate shortcuts in many cases, our method still significantly outperforms it across all tests. Moreover, IRM performs worse than the baseline on small datasets, such as Chest-XRay (w/SWIN) and GYAFC (w/LLaMA). We attribute this to IRM assigning excessive training weight to features that remain invariant, which prevents other useful features from being accurately identified and utilized.\n\n\n**2. Adding a Visual Example of Semantic Bias**\n\nWe used PCA to reduce the BERT embeddings of the Yelp dataset to two dimensions and visualized three clusters, as shown in the Anonymous github we attached in the paper. In the image (https://anonymous.4open.science/r/SCISSOR-3F55/unb.svg), the colors represent cluster labels, while in the image (https://anonymous.4open.science/r/SCISSOR-3F55/unb_l.svg), colors indicate task labels (positive/negative).\n\nWe observe that different clusters are located in distinct regions. Furthermore, the task labels within each cluster are imbalanced: in two of the three clusters, positive samples are more prevalent, while in the remaining cluster, negative samples dominate. Nevertheless, the total number of positive and negative samples across all three clusters is equal.\n\nIn this scenario, negative samples belonging to clusters with more positive samples are theoretically more likely to be misclassified as positive by the models (as suggested by the Lemma 1 and Lemma 2), leading to the semantic shortcut we discuss.\n\nFurthermore, in the image (https://anonymous.4open.science/r/SCISSOR-3F55/pca_SCISSOR.svg), we show the semantic representations of samples after being remapped by our debiasing module. Through using SCISSOR, we observe that the task-irrelevant cluster information has been successfully removed. In contrast, classification information is preserved, naturally leading to a distinct classification boundary among the samples. To better illustrate the separability of the samples here, we visualize the three-dimensional PCA results of the output of our debiasing module (https://anonymous.4open.science/r/SCISSOR-3F55/pca_SCISSOR_3d.svg).\n\n**3. Experiments on Large-Scale benchmarks**\n\nThank you for these valuable pointers. We are running experiments on the WILD and DomainBed benchmarks. However, due to the large scale of these experiments, we may not be able to obtain results within the rebuttal period (we might make it within the discussion period). Nevertheless, we emphasize that our current experiments already include 4 commonly used datasets, which we believe provide a solid basis for evaluating our approach and demonstrating its reliability.\n\n[1] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant risk minimization. arXiv preprint arXiv:1907.02893.", "claims_and_evidence": "The paper’s main claim that semantic bias can be mitigated through cluster-aware Siamese networks, is grounded in both theoretical observations and empirical experiments.", "methods_and_evaluation_criteria": "The semantic cluster intervention makes sense for suppressing shortcuts, with its performance evaluated using accuracy and F1 metrics on multiple datasets that are suitable for this assessment.", "theoretical_claims": "I reviewed the lemma discussing the existence of semantic bias and the theory-grounded observations, and they appear logically sound and correct to me.", "experimental_designs_or_analyses": "The experimental designs involve six models, and the results and analysis demonstrate both the validation of semantic shortcuts and the effectiveness of the proposed method.", "relation_to_broader_scientific_literature": "This work is closely related to shortcut learning for generalization under distribution shifts, biases, and spurious correlations issues that are critical in the field of robust machine learning.", "essential_references_not_discussed": "Mitigating shortcut learning and spurious correlations is fundamental to achieving robust machine learning. Existing literature (for example [1, 2]) has introduced various strategies to address these challenges. A thorough, in-depth comparison with such works is necessary to highlight the position of the proposed approach in the broader research field.\n\n[1] Invariant Risk Minimization. 2019.\n\n[2] Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization. 2020.", "other_strengths_and_weaknesses": "Advantages\n\n1. This work addresses the critical issue of shortcut learning in robust machine learning, which is an important and challenging research area.\n\n2. The proposed method appears reasonable and is supported by experimental details that demonstrate its effectiveness.\n\nWeaknesses\n\n1. The theoretical analysis primarily focuses on identifying biases and discussing related observations. A more in-depth and stylized analysis of the cluster-aware Siamese network for robust classification would further strengthen the theoretical foundation.\n\n2. Figure 1 illustrates sentiment classification, but more visualizations using real data would help showcase the semantic space and better demonstrate the proposed approach’s effectiveness.\n\n3. A comprehensive evaluation on large-scale, real-world datasets (such as [1, 2]) would provide stronger evidence of the method’s practical applicability.\n\n[1] WILDS: A Benchmark of in-the-Wild Distribution Shifts. ICML 2021.\n\n[2] In Search of Lost Domain Generalization. 2020.", "other_comments_or_suggestions": "None", "questions_for_authors": "Refer to the Weaknesses section for more detailed questions.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hNzizbH4vK", "reviewer": "Reviewer_347M", "summary": "This paper proposes SCISSOR, a debiasing approach that mitigates semantic biases in classifiers by disrupting semantic clusters that create shortcut learning. Using a Siamese network with Markov Clustering, it creates contrastive learning pairs to remap the semantic space, and through experiments, showed strong improvements across six models and four datasets.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive feedback on our work. We sincerely appreciate your time and support. We'll address any further questions you should have during the discussion period to improve our paper, and make it through the finish line.", "claims_and_evidence": "The claims made in the submission are supported by clear and convincing evidence.", "methods_and_evaluation_criteria": "The benchmark datasets and model selection cover vision and language, making the results broadly applicable. The evaluation criteria align well with the problem of semantic bias mitigation.", "theoretical_claims": "The paper outlines two lemmas that explain how semantic clusters affect classification. Formal proofs are also included in the appendix.", "experimental_designs_or_analyses": "The experimental design is well-structured, which includes comparison with related methods across a wide range of benchmarks and backbones. Multiple metrics are reported, and a computational efficiency analysis is also included.", "relation_to_broader_scientific_literature": "In comparison to previous works, this work is motivated by the fact that balanced data can still have semantic biases. The proposed method is instead designed to remap the embedding space itself, complementing both dataset-centric and DRO-based methods by specifically targeting label-skewed clusters.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Please see sections above.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nWHEoTKBnB", "reviewer": "Reviewer_eygs", "summary": "## Summary \nThis work aims at learning an adapter on pretrained representation to \"filter out classification-irrelevant semantic features\", to help out-of-distributation robustness. The author proposes a compliciated approach incorporating clustering, reweighting, contrastive learning, and creating “intermediate sample\". \n\n\n\n## Strongthness\n- Out-of-distributation generalization is a challenging and interesting topic. \n\n## Weakness \n\n- This work claims \" first to identify and demonstrate, both theoretically and empirically, that imbalances in the semantic distribution of samples can also lead to the shortcut problem.\" However, imbalance and shortcut learning is not new. Theorical work includes [1]. Experimental works includes GroupDRO and [3].\n\nPlease note that \"semantic distribution\" notion in this paper doesn't make the claim novel. Because one can treat the \"semantic embedding\", i.e. the representation of pretrained modek, as a preprocessed input. And apply all the imbalance techniques in the literature. \n\n- The proposed method is complicated and hard to undertsand. Maybe an Algorithm table help. There are many unclear parts in the method, including how to choose intermediate example? How to support dataset with multiple classes? \n\n- Lack of experimental comparision between naive baselines, such as simple reweighting, IRM [2] and many variations. Table 2 and Table 3 are not very helpful.\n\n\n[1] Chaudhuri, K., Ahuja, K., Arjovsky, M. &amp; Lopez-Paz, D.. (2023). Why does Throwing Away Data Improve Worst-Group Error?. Proceedings of the 40th International Conference on Machine Learning\n\n[2] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant risk minimization. arXiv preprint arXiv:1907.02893.\n\n[3] Kirichenko, P., Izmailov, P., & Wilson, A. G. (2022). Last layer re-training is sufficient for robustness to spurious correlations. arXiv preprint arXiv:2204.02937.", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1. On the Lack of Novelty**\n\nWe respectfully clarify that, to our knowledge, shortcut learning stemming from a semantic imbalance in pre-trained models (which requires unsupervised analysis) is not widely studied. The theoretical reference [1] you mentioned deals with label quantity bias, rather than semantic distribution. In fact, our experiments (lines 192-196) use balanced label distributions. Likewise, GroupDRO also focuses on imbalances in categories, not the distribution of semantic features. The debiasing in our paper refers to mitigating shortcuts which universally across all groups in the training set, rather than focusing on the worst group. Meanwhile, [2] requires prior knowledge of shortcuts and access to an unbiased dataset, both of which can be impractical in real-world scenarios [3][4][5].\n\nConsequently, our assertion that “an imbalance in semantic distribution can lead to shortcuts” remains valid and distinct from the biases explored in [1], GroupDRO, and [2].\n\n**2. On Baseline Comparisons**\n\nAs noted in our Introduction, existing shortcut-mitigation research typically addresses label imbalance or focuses on token/pixel-level biases. Please note that direct reweighting methods, for example, are not applicable here because our training samples are already balanced by label. This underscores our key statement: “Shortcuts can persist even when labels are balanced.”\n\nTo address concerns about baselines, we have added a comparative experiment with IRM; please see our “to reviewer gyYJ” response for details. We also compare our approach with RAZOR, a leading method that already surpasses several simpler baselines. Because our method outperforms RAZOR, we did not repeat comparisons with those baselines, especially since their focus is on token-level biases, whereas our work targets semantic-level biases.\n\n**3. On the Usefulness of Tables 2 and 3**\n\nIn response to concerns about the value of Tables 2 and 3, our main motivation arises from the imbalance in semantic distribution, which the Hopkins Statistic measures effectively. By comparing Tables 4 and 5, we illustrate that our method’s effectiveness increases as semantic imbalance grows, demonstrating that our semantic debiasing approach genuinely shifts how samples are represented.\n\n**4. Clarity Concerns**\n\n**4.1 Choosing Intermediate Samples**\n\nIn lines 214-215, we explain that “Intermediate samples share the same label and cluster as the anchor,” and this process is also illustrated in Figure 2. Nevertheless, we will try to better highlight this aspect in our paper.\n\n**4.2 Multi-Label Classification**\n\nOur “Problem Formulation” section does not restrict us to binary classification. Figure 1 uses a binary example solely for illustrative purposes. In fact, we have already evaluated multi-label scenarios in the paper, such as the Not-MNIST dataset, which has ten labels (A-J) as noted in lines 291-293.\nFinally, we emphasize that shortcut learning is multifaceted; recent works on style-based shortcuts [6] and in-context learning shortcuts [7] highlight various dimensions of this issue. We believe their contributions are valuable, as they address different forms of shortcuts that are similar to our notion of semantic shortcuts.\n\nFinally, we emphasize that our work tackles semantic shortcuts, a fundamentally different challenge than label-based or count-based bias mitigation. Addressing these subtler cues demands deeper scrutiny than simply balancing label distributions or removing obvious spurious correlations. Consequently, our approach necessitates unsupervised techniques to effectively uncover and mitigate these hidden biases.\n\n[1] Chaudhuri, K., Ahuja, K., Arjovsky, M. & Lopez-Paz, D. (2023). Why does Throwing Away Data Improve Worst-Group Error? Proceedings of the 40th International Conference on Machine Learning\n\n[2] Kirichenko, P., Izmailov, P., & Wilson, A. G. (2022). Last layer re-training is sufficient for robustness to spurious correlations. arXiv preprint arXiv:2204.02937.\n\n[3] Weizhi Xu, Qiang Liu, Shu Wu, Liang Wang (2023), Counterfactual Debiasing for Fact Verification\n\n[4] Shuo Yang, Bardh Prenkaj, Gjergji Kasneci. (2025). RAZOR: Sharpening Knowledge by Cutting Bias with Unsupervised Text Rewriting\n\n[5] Zeming Chen, Qiyue Gao, Antoine Bosselut, Ashish Sabharwal, Kyle Richardson. (2024). DISCO: Distilling Counterfactuals with Large Language Models\n\n[6] Yuqing Zhou, Ruixiang Tang, Ziyu Yao, Ziwei Zhu. (2024). Navigating the Shortcut Maze: A Comprehensive Analysis of Shortcut Learning in Text Classification by Language Models\n\n[7] Joonwon Jang, Sanghwan Jang, Wonbin Kweon, Minjin Jeon, Hwanjo Yu. (2024). Rectifying Demonstration Shortcut in In-Context Learning", "claims_and_evidence": "check summary", "methods_and_evaluation_criteria": "check summary", "theoretical_claims": "check summary", "experimental_designs_or_analyses": "check summary", "relation_to_broader_scientific_literature": "check summary", "essential_references_not_discussed": "check summary", "other_strengths_and_weaknesses": "check summary", "other_comments_or_suggestions": "check summary", "questions_for_authors": "check summary", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i6fkVbQyGW", "reviewer": "Reviewer_2fdf", "summary": "This paper presents a novel debiasing architecture that leverages siamese networks and clustering techniques to mitigate spurious correlations in learned embeddings. The proposed approach remaps the embedding space to discourage unwanted dependencies between inputs and outputs while preserving meaningful semantic clusters.\nA key advantage of this method is that it eliminates the need for extensive data augmentation or text rewriting, making it a more efficient alternative for debiasing. \n\nThe architecture introduces a debiasing module designed to disrupt semantic shortcuts, which are often responsible for model biases. This module is integrated between a pre-trained model and the classification head.\nExtensive experiments on both image and text datasets demonstrate the effectiveness of this approach, showcasing its ability to enhance generalization by reducing the impact of spurious correlations.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1. On Clustering Method (with Larger Models)**\n\nWe conducted an ablation experiment using DBSCAN with LLaMA3, the largest model we used. The results are as follows:\n\n| Dataset        | ACC (GYAFC) | F1 (GYAFC) |  | ACC (Yelp) | F1 (Yelp) |\n|---------------|------------|------------|--|------------|-----------|\n| LLaMA3         | 89.37       | 0.89       |  | 94.57      | 0.95      |\n| LLaMA3 (w/ DBSCAN)  | 89.91       | 0.90       |  | 95.41      | 0.95      |\n\nWe observed that the improvement of DBSCAN and MCL on LLaMA3 is similar, both being approximately 1%. This indicates that SCISSOR exhibits robustness to variations in clustering algorithms.\n\n**2. On Semantic Bias and Initial Embeddings**\n\nSince most contemporary debiasing works rely on pre-trained language models, we followed their works. We will consider debiasing methods in future work that do not rely on initial information.\n\n**3. On Background Colors in the Table in Step2 of Figure 2**\n\nWe are sorry for making the confusion. The background colors are used to distinguish samples (i.e., Anchor, Positive, Intermediate, Negative). Their function is same to the color of the \"Sun\" and \"Moon\" markers. We will improve the readability of Figure 2 in the camera-ready version.", "claims_and_evidence": "The authors have done a good job in presenting lemmas on the existence of semantic biases, deriving insightful theoretical observations, and validating their claims empirically. The combination of theoretical derivations and experiments strengthens their argument and provides evidence for their claims.", "methods_and_evaluation_criteria": "While the paper effectively presents its method, there are some concerns regarding the reliance on Markov clustering. If the clustering does not accurately reflect the semantic structure of the embeddings, it may fail to suppress shortcuts effectively. Exploring alternative clustering methods such as Spectral Clustering, DBSCAN, or Gaussian Mixture Models could enhance robustness, particularly for larger models.\n\nWhile the authors claim that larger models exhibit a weaker cluster effect, as indicated by a high Hopkins statistic, it is worth considering that this may be due to the difficulty of identifying clusters in high-dimensional spaces rather than an inherent weakening of the clustering effect itself. This could be a reason why SCISSOR seems to work best on smaller architectures.", "theoretical_claims": "The paper’s theoretical contributions are well-structured, particularly in defining semantic biases and how they influence shortcut learning. \n\nI have skimmed the proof of the lemmas in the appendix.", "experimental_designs_or_analyses": "The experiments effectively demonstrate the performance improvements of SCISSOR across multiple benchmarks, showing significant gains over baselines. However, it would be valuable to evaluate alternative clustering techniques and compare their impact on model performance, particularly for larger architectures where clustering appears weaker.", "relation_to_broader_scientific_literature": "This paper contributes to literature on spurious correlations and shortcut learning. The methodology aligns with recent work on debiasing techniques and latent space interventions.", "essential_references_not_discussed": "Not to the best of my knowledge.", "other_strengths_and_weaknesses": "Strengths: The proposed model is novel, especially in how it remaps the latent space to suppress spurious clusters. It also forces the model to focus on real discriminative features rather than cluster membership, improving generalization.\n\nWeaknesses: The reliance on pre-trained embeddings being well-structured at the start of training could limit the method’s applicability to scenarios where embeddings are noisy or uninformative.", "other_comments_or_suggestions": "1. Typos:\n    1. Line 127, left column: \"A employs LLMs\"\n    2. Line 266 right column: \"debasing\" -> \"debiasing\"\n\n\n2. What do the different background colors in the table in step2 of figure 2 represent?", "questions_for_authors": "Have you considered alternative clustering techniques to improve performance on larger models?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper proposes SCISSOR, which mitigates semantic bias in pre-trained models by remapping latent spaces through cluster-aware Siamese networks. The method discourages shortcut learning by disrupting imbalanced semantic clusters, without requiring data augmentation or rewriting. The method is evaluated across four benchmarks and six models in both vision and NLP domains.

The paper addresses an underexplored aspect of shortcut learning by studying semantic biases arising from latent space imbalances. The reviewers largely agree that the method is novel, well-motivated, and theoretically grounded. The experimental evaluation is in general thorough to show the consistent improvements over existing debiasing baselines such as RAZOR and IRM. Reviewers appreciated the plug-and-play design, the breadth of evaluation, and the evidence that SCISSOR works across modalities and model scales.

The main concerns come from reviewer eygs questioning the novelty and clarity of the method. The authors, however, did a good job providing detailed and convincing rebuttals, clarifying distinctions from prior works focused on label imbalance and showing that simple reweighting or IRM are insufficient for their setting. In addition, the authors addressed gyYJ’s concerns by adding comparisons to IRM, and provided additional visualizations to illustrate semantic bias. While the large-scale evaluations were not completed, the current results already demonstrate sufficient evidence on the effectiveness of the proposed method. Some reviewers noted the reliance on clustering and initial embeddings, but ablations show the method's robustness across choices.

Considering the above aspects, the AC recommends acceptance to ICML.
Decision: Accept (poster)