GRADIEND: Feature Learning within Neural Networks Exemplified through Biases
Authors: Jonathan Drechsel
Steffen Herbold
Venue: ICLR 2026 Poster (2026)
Keywords: Feature Learning
Bias Mitigation
AI Fairness
Language Models
Primary area: interpretability and explainable AI
TL;DR: Using model gradients, we learn a single feature neuron that encodes a desired feature along an orthogonal axis (e.g., gender) and show that this can debias models.

Abstract: AI systems frequently exhibit and amplify social biases, leading to harmful consequences in critical areas. This study introduces a novel encoder-decoder approach that leverages model gradients to learn a feature neuron encoding societal bias information such as gender, race, and religion. We show that our method can not only identify which weights of a model need to be changed to modify a feature, but even demonstrate that this can be used to rewrite models to debias them while maintaining other capabilities. We demonstrate the effectiveness of our approach across various model architectures and highlight its potential for broader applications.

Published as a conference paper at ICLR 2026
GRADIEND: FEATURE LEARNING WITHIN NEURAL NETWORKS EXEMPLIFIED THROUGH BIASES
Jonathan Drechsel & Steffen Herbold
Faculty of Computer Science and Mathematics
University of Passau
Passau, Germany
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ABSTRACT
AI systems frequently exhibit and amplify social biases, leading to harmful conse- quences in critical areas. This study introduces novel encoder-decoder approach that leverages model gradients to learn feature neuron encoding societal bias information such gender, race, and religion. We show that our method can not only identify which weights a model need to be changed to modify a feature, but even demonstrate that this can be used to rewrite models debias them while main- taining other capabilities. We demonstrate the effectiveness our approach across various model architectures and highlight its potential for broader applications.
INTRODUCTION
Modern Artificial Intelligence (AI) systems encode vast amounts of information in their internal parameters. Some of these parameters correspond to semantically meaningful features, such as linguistic structure social concepts (Jawahar et al.) 2019| Gandhi et al.) (023). Understanding and controlling these features critical for improving model interpretability, robustness, and fairness. While prior work has uncovered individual or groups of neurons that correlate with specific features (Bricken et al. 2023), systematically learning targeted features remains a challenge.
We propose novel approach to learn features in language models by leveraging gradients from feature-related input. We hypothesize that these gradients contain valuable information for identifying and modifying model's behavior related to feature. Unlike existing approaches for extracting monosemantic features (c.g., Bricken et al. 2023), our approach enables the lcarning of feature neuron with a desired, interpretable meaning. The feature neuron is modeled bottleneck in simple encoder-decoder architecture for model gradients. The decoder essentially learns what parts of the model needs to updated to change feature.
One particularly important class of features relates to societal biases such as gender. Al is often seen neutral tool without personal preferences or biases (Jones-Jang & Park 2022] Jiang 2024), but it can still exhibit and even amplify bias (Nadeem et al. 2020). with harmful impacts in crucial areas such healthcare and hiring (Buolamwini & Gebru 2018 Ferrara 2023 For instance, Amazon's Al-powered hiring tool, trained on resumes from a male-dominated tech industry, was found to favor male candidates, penalizing resumes referencing women's colleges (Dastin 2022). This underscores a crucial problem: AI models, though seemingly neutral, can inherit and amplify real-world biases.
Recent research has explored how bias appears language models (Nemani et al.) 2024; Gallegos et al. 2024). Proposed solutions include specialized training (Zmigrod et al. 2019 Webster et al. 2021), pruning biased neurons (Joniak Aizawa 2022), post-processing steps that adjust model outputs without modifying internal parameters (Ravfogel et al. 2020; Liang et al. 2020 Schick et al.
2021), and methods to measure the bias (May et al. 2019 Nadeem et al. 2021).
This paper investigates two hypotheses: (H1) It is possible to learn targeted a feature neuron from the model's 's gradients with a desired interpretation, such as gender (e.g., distinguishing female and male inputs). (H2) This feature neuron be used modify model behavior related to the feature (e.g., bias) without negatively affecting other capabilities. By exploring these hypotheses, we demonstrate the potential of targeted feature learning and achieve SoTA results for gender debiasing when

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Published as a conference paper at ICLR 2026
Data
GRADIEND
Encoded Value
Template Text (GENTER): (NAME] explnined as best [PRONOUN]
Encoder
Random Nome (NAMEXACT): Alice
femule ncucal
Training Instance: Alice explained the vision as best [MASK] could.
she / +
V. Wm
fomale
[MASK]
Wm
Model
MLM Gradients
GRADIEND
Model
GRADIEND Decoder
Changed Model
Encoder
Decoder
Jeamiin rate
*
fenture
Wm
E Wm
Wi
54 Wm
We: R..
Wa
dee(R)
V2 Win
dae(h))
Wi..
(a) Training Phase:
Learning to encode the feature gender and how to change a model's gender bias.
(b) Inference Phase: Evaluating the fea-- ture neuron and modifying gender bias.
GRADIent ENcoder Decoder (GRADIEND) - Targeted learning of a single scalar feature neuron using orthogonal gradient inputs, shown with an example for gender bias.
Figure 1:
using GRADIEND together with INLP (Ravfogel et al., 2020), evaluated against broad set of debiasing methods and their combinations. Although this study focuses on gender, race, and religion bias, the proposed encoder-decoder approach is generic and should also be able to learn other features.
For clarity, in this study, gender is treated binary (while acknowledging and respecting non-binary gender identities). Similarly, we focus on a limited set of races Asian, Black, and White and religions Christian, Jewish, and Muslim, based on prior research (Meade al. 2022)
RELATED WORK
This section reviews interpretable feature learning and existing methods for debiasing transformer models, while additional techniques for measuring bias are discussed Appendix C.5
2.1 INTERPRETABLE FEATURE LEARNING
Interpretable feature learning aims identify and understand the internal representations of neural networks, focusing on how individual neurons groups of neurons relate to specific concepts. Early methods focused on visualizing learned features through saliency maps (Simonyan al., [014] and activation maximization (Frhaan et al., 2009), highlighting the influence of inputs model predictions. Recent advancements focus on separating networks into semantically meaningful units like individual neurons circuits (Olah et al. 22200). Research monosemantic neurons those aligned with single natural feature offers clearer and more interpretable insights compared to polysemantic ones (Jermyn al. 2022). Bricken al. (2023) proposed to learn unsupervised Sparse AutoEncoder (SAE) that extracts interpretable features in a high-dimensional feature space, which are analyzed for semantical meaning based on their behavior. Follow-up studies (Templeton al. 2024 improved scalability and identified specific features such as gender-bias awareness feature in Claude Sonnet (Anthropic 2024). However, this approach requires learning numerous potential features and testing for meaningful interpretations, leaving it uncertain whether desired feature will actually arise. Another limitation of SAES that they do not consider the model parameters (i.e., weights) directly, but rather only the activation of neurons. This means that rewriting of models is not directly possible and can only be achieved at inference time by changing model activations. In comparison, while we speak of learning of neurons well, our proposed GRADIEND method works by learning weights associated with features directly in a manner that enables rewriting and that allows us to target specific features. Moreover, while SAEs are typically trained for single transformer layer even only a subset of one (Bricken et al. 2023 Brinkmann et al. 2025). GRADIEND can be applied to all parameters across all layers.
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2.2 TRANSFORMER DEBIASING TECHNIQUES
Various techniques have been proposed to mitigate bias in transformer language models (see, e.g.. Li et al. )023), either by creating debiased models by changing weights through post-processing adjustments. This section introduces a subset of representative techniques relevant to this study.
Counterfactual Data Augmentation (CDA: Zmigrod al. 2019 Lu et al. 2020) is a straightforward method which swaps bias-related words consistently within training corpus (e.g., replacing helshe for gender bias), enabling further training on balanced dataset. [Webster et al. (2021) found experimentally that increasing DROPOUT during pre- pre-training effectively reduces bias.
The Iterative Nullspace Projection (INLP: Raviogel et al. 2020) is a post-processing debiasing method by iteratively training a linear classifier of the property to be removed (e.g., gender) based on model embeddings and subtracting the classifier's nullspace from the embeddings remove property- related information. Its successors, RLACE (Ravfogel et al. (022) and LEACE (Belrose et al., 2023). improve nullspace estimation with more compact and effective projections. SENTDEBIAS (Liagg et al.] 2020) estimates linear subspace associated with bias by using CDA to generate sentence pairs with swapped terms (e.g., helshe) and debiases sentence embeddings by subtracting their projection onto this subspace. SELFDEBIAS (Schick et al. (021) addresses bias in generated text by running inference with and without a bias-encouraging prefix, downweighting tokens favored in the biased version. However, this approach is unsuitable for downstream tasks like GLUE (Wang et al.) 2018). In Section [5.4 we compare our method with the other debiasing techniques and their combinations on GLUE and on SuperGLUE Wang al. (2019), extending prior work focused on GLUE.
3 METHODOLOGY
We introduce a novel approach for targeted feature learning and bias modification. Our method utilizes a simple encoder-decoder architecture that leverages gradient information to encode a gender- related scalar value. This scalar is then decoded into gradient updates, which are used to adjust the model's bias toward the encoded feature value. An overview of the approach is illustrated in Figure
3.1 MOTIVATION
Gradient-based explanation methods, such as Grad-CAM (Selvaraju et al., 2017) and Integrated Gradients (Sundararajan et al. (017). have proven effective in providing insights into model's internal workings (Chen et al. 2020: Selvaraju et al. 2020 Lundstrom et al. 2022). highlighting which parts of the model were crucial to specific prediction. During the training of neural networks, the optimizer inherently determines which neurons require updates, specifically those that contributed incorrectly to the model's 's output. We leverage this mechanism through Token Prediction Task (TPT) whose masked token is sensitive to a chosen feature (e.g., gender, race, religion). For encoder-only models, we use Masked Language Modeling (MLM: Devlin et al. 2018), and for decoder-only models, we use Causal Language Modeling (CLM; Radford et al. 2019a). For clarity, the following explanations focus on the MLM variant, with details on adapting the task CLM (c.g., using only left-side context before the [MASK]) provided in Appendix D.3
To illustrate, consider the binary gender case. Suppose we have sentence where the masked token refers to a gendered pronoun determined by a name, e.g., "Alice explained the vision as best [MASK] could ". Here, she is the factual target (consistent with the context), while he serves as the counterfactual target. For features with more than two classes, the counterfactual notion naturally generalizes to an orthogonal target: any instance of the same feature that differs from the factual one (c.g., another race religion) can serve as alternative target.
By using factual-orthogonal evaluations for two feature classes, gradient differences are computed to isolate feature-related updates by eliminating non-feature-related changes common to both cases. This difference yields two inverse directions: strengthening or mitigating bias with respect to the chosen feature classes), depending on the gradient order. In the mitigating direction, the factual feature-related updates are eliminated, effectively removing the established factual associations, while the orthogonal updates emphasized facilitate the learning of new, orthogonal associations.
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3.2 GRADIEND
In general, we aim to learn how to adjust model parameters to achieve desired factual or orthogonal state. We hypothesize that the gradients contain the necessary information for this purpose and that the feature changing behavior can be controlled via a learned neuron.
Let feature be represented by 2 orthogonal classes C (Ci...., C,). For training, select two distinct classes A, B E C and consider TPTs where the masked token corresponds to either A (factual A, orthogonal B) to B (factual B3, orthogonal A).
Let W m E R denote the n model parameters for which the feature is learned.
For an example with factual class C E {A, B} and orthogonal class C" E (A, B) \ (C), we define three types of gradients: (1) gradients from the factual masking task Wmm, m (i.c., the target belongs to C), (2) gradients from the orthogonal masking task V Wm (i.e., the target belongs to C"), and (3) the difference between these two gradients V- Wm V. W m V W Here, V. J.W,, represents a vector R", where each component corresponds to the gradient for the parameter this position. We frame the problem as a gradient learning task to predict the gradient difference V._ W. from the factual gradients V. Wm:
Learn f s.t. f( W.) == W
For this study, propose simple encoder-decoder structure f = dec Enc, where:
enc(V_ V_ mm) = tan W,W. + be) =: h E IR.
dec(h) = h Wa + ba
V.
Here, We, Wa, ba E R" and b. R are learnable parameters, resulting in total of 3n+ parameters. We refer to this approach as GRADIent ENcoder Decoder (GRADIEND).
3.3 GRADIEND FOR DEBIASING
While GRADIEND is defined for orthogonal class pairs of any feature, restrict the following proof of concept to the bias types gender, race, and religion. Gender is treated binary in this study (d = 2; C1 Female and C2 Malc), while race (C1 Asian, C2 = Black, and C3 White) and religion (C1 = Christian, C2 = Jewish, and C3- = Muslim) are considered with
classes
HII suggests that the factual and counterfactual masking tasks guide the encoder to produce a feature-related sealar h, representing the orthogonal axis between two chosen classes A and B. Hypothesis (H2) asserts that dec(h) can adjust the model's bias along this orthogonal axis, e.g., by choosing a specific feature factor h and learning rate a to update the model parameters follows:
In this setup, hypothesis
(1) Experiments show that feature-related inputs are mostly mapped to values close to -1 and +1, corresponding the classes A and B or vice versa. WLOG, assume and B are ordered lexicographically and that positive values of h represent A while negative values represent B. This post-hoc standardization enables consistent definitions and visualizations across experiments.
Wm == Wm + a dec(h).
4 DATA
For each bias type, we filter existing datasets to derive masked texts where the mask corresponds to the bias target terms. For gender, these targets are the pronouns helshe, determined solely by the gender of a preceding name. We augment a BookCorpus-derivel dataset (Zhu et al. 2015) using names as templates diversify the model gradients, and filter texts where gender could be inferred from other words. For race and religion, we follow simplified procedure similar to Mcade et al. (2022) using CDA: From English Wikipedia, we retain only sentences that contain one of their predefined bias-attribute words (e.g., Jewish, African). These attribute words are then masked to generate bias-specific gradients. This produces dataset for each pair of race or religion classes, treating one as factual and the other as orthogonal. Combining both directions for pair yields the training dataset for that pair. For brevity, we denote by T the dataset associated with a particular GRADIEND instance. To evaluate language modeling performance independently of bias, create BIASNEUTRAL, a BookCorpus subset without bias target words. Full dataset generation details are in Appendix B
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Female
Made
TNEUTRAL
BIASNEUTRAL
Label
0.5
-0.5
-1.0
BERT
BERTiee
DistiBERTT
RoBERT's Model
GPT-2
LLaMA
LLaMA-Instreet
Figure 2: Distribution of encoded values for all gender GRADIEND models across different datasets. The yellow dots indicate the expected label used for Cor
EXPERIMENTS
In this section, we evaluate GRADIENDS based on seven base models: BERT base and BERT large (Devlin et al. 2018), RoBERTa (Liu et al. 2019), DistilBERT (Sanh al. 2019). GPT-2 (Radford et al. 2019b), and two LLaMA-3.22 33.2-3B models (Grattafiori et al.) 2024) one plain (LLaMA) and instruction fine-tuned (IJI Innssttt,,, covering broad range of transformer variants. All datasets T are split into training, validation, and test sets. Metrics are reported for the test split (or the entire dataset if not split), unless stated otherwise.
5.1 TRAINING
Each training step processes batch of TPTs with target class chosen uniformly random, ensuring that only gradients for that single target contribute to the GRADIEND input within a training step. To ensure that debiasing affects the language model itself and not just the token prediction head, we exclude the prediction layers from the set of GRADIEND parameters (i.e., the MLM and CLM heads), while using all other weights, including the embeddings and the attention and MLP weights of every transformer layer. Implementation details, hyperparameters, and initialization are described in Appendix D
5.2 FEATURE ENCODER
We evaluate whether the GRADIENDS encode the intended feature (hypothesis (HI) by analyzing their encoder outputs on (1) training- like data (i.e., same target tokens as seen during training) and (2) neutral data (i.e., tokens unseen in training and unrelated to the feature). We expect training tokens to yield consistent encodings near + (due to the tanh activation), and neutral tokens to map near 0, the natural midpoint between the class extremes.
Figure S shows the encoded values for gender across all models, while Figure 3 presents results for race and religion for BERT base (other models and ablation studies on gender feature stability and data/token variability are in Appendix feature-related gradients, and 7 NEUTRAL where feature unrelated tokens BIASNEUTRAL. For sentences as T. We also include the independently derived neutral dataset B race and religion, training data from other classes are additionally reused for evaluation as well (e.g.. Asian - Black for an Asian/White model). Within each evaluation, all subsets are balanced by downsampling to the size of the smallest split.
For evaluation, we use the T test split to capture masked in the same
Across all models, encoders successfully separate the two training classes, while neutral tokens tend to cluster around 0, though this classification is less precise for some GRADIENDS. Importantly, neutral masks were not seen during training, showing that the encoder did not only learn binary feature, but rather a polar one, with opposite ends the polar scale used during training.
The behavior unseen classes further reveals interesting biases. For example, the Black/W Chik/ models often resemble a White vs. Non-Worn White distinction, possibly reflecting imbalances towards White dominated data during their pretraining (Figure Bap. Similarly, the religion models suggest that Judaism and Islam are encoded as more similar to each other than to Christianity (Figure 33b).
Table quantifies these findings by reporting Pearson correlations (Cohen et al. 2009) for the training- like data (Cory; only #1 labels) and for all evaluations shown in Figures 2 and 5 (Cor including neutral labels of 0). All models achieve strong performance Cory for gender, but ..L.M--base

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Published as a conference paper at ICLR 2026
Mini
Blxd
Black
Asian
Wille a Asino
TAEITRAL
Not ued durag triiiig
Label
Black
White Black
BIA BIASMRUTRAL
Usad
Blick/White
0.2
-0.3
-10
Block
White
Juhnn
Neatre.
(a) Race (see Figure 5a for all models).
Cleatio Joniaz
Janah Christion
Mialin Chaitau
TArUTral
Not usdd daras, trasiig
9
Label
Chrizion Mudim
Jeeral Muelim
Mralin Jorrds
(Jad during traiing
Anistiin//ewwil
Cluistion
Jewisly Muslim
0.2
Jowidh
Misdin
Chratiaa
Muslin
Christin
Jewish
Mandim
Nestmi
(b) Religion (sec Figure for all models).
Figure 3: Distribution of encoded values for different datasets of the BERT base GRADIEND models for race and religion. The yellow dots indicate the expected label used for Corgne-
Table 1:
Pearson correlation between encoded values and labels of Figures 2 and All values are scaled by 100. Best values per column are printed in bold.
Gender
Race
Religion
Mcan
Female/Mtll
Asdan/Black
Asian/White
Black/White
Cheist./Jww.
Christ/Mus.
Jew/Muslin
Cotyr
Cocyr
Cocy
Comrre
Cory
Cocpae
Cotyr
Cary
Cory
BERT
71.3
96.3
84.4
98.6
92.3
92.2
88.4
BERT
90.8
66.0
98.2
94.6
96.7
89.1
96.5
92.0
97.2
92.8
98.4
91.8
98.8
96.6
96.7
89.0
DistiBERT
100.0
86.0
99.7
92.4
96.2
98.5
83.2
98.9
91.5
90.0
99.6
94.9
98.9
89.1
RoBERTa
100.0
95.3
96.2
83.6
95.6
82.7
98.0
85.4
99.5
92.6
99.5
90.8
97.8
94.0
98.1
89.2
GPT-2
100.0
98.4
97.8
87.5
98.5
91.8
98.3
84.7
98.4
97.1
98.6
96.2
99.2
98.9
98.7
93.5
LLaMA
99.3
98.3
90.
79.9
88.4
78.8
88.4
78.
89.0
79.0
78.6
72.3
82.1
73.8
88.0
80.0
LLaMA-L
99.0
97.6
89.7
73.6
87.7
63.7
84.8
72.4
90.3
80.4
71.4
60.0
86.3
71.0
87.0
74.1
Mean
97.8
87.5
95.9
86.5
94.2
81.6
94.7
84.7
96.0
89.4
92.2
84.2
94.8
89.3
95.1
86.2
models perform noticeably worse for race and religion, likely due to their larger tokenizer: gender targets (he/she) remain single tokens, whereas many race and religion targets are split into multiple tokens, unlike in smaller models where most targets are single-tokenized (see Appendix D.3). GPT-2 performs best overall, particularly on the generalization metric Cor mapping neutral inputs reliably near O. The most challenging distinction for religion is Christian/Muslin, reflecting their greater textual overlap and semantic similarity, consistent with prior studies (Nandan et al. (225)).
The GRADIEND models consistently learn interpretable feature neurons, mapping target classes to +1 and neutral input mostly near 0, thereby supporting hypothesis (H1)
5.3 DECODER BIAS- CHAS-CHANGER
We investigate how the learned representation of the decoder can change model bias. The model adjustment is controlled by two parameters: the scalar input to the decoder network (feature factor) and the learning rate a, which scales the decoder output before adding the model weights. To assess the impact of these parameters, we evaluate the GRADIEND models across grid of 15 feature dec(h).
factors and 16 learning rates, modifying the model weights WT, W..
For the resulting models, we require three key properties: (1) Their overall language modeling performance should remain close to the original model. to tokens from both classes A and B. (3) Both A and should retain sufficiently high probabilities avoid trivial solutions (e.g., collapsing to near-7ero).
(2) They should assign balanced probabilities

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Published as a conference paper at ICLR 2026
352.53 1.5323385 23383 5.
Leaning Bate
Teaning Ratr
(a) (P(P)t1
(b) P(M))
(c) LMS 111
(d) BalancedBS f
Figure 4: Metries for changed models based on the BERT base gender GRADIEND with varying feature factor and learning rate. The cells with the best BalancedBS D. FemaleBS D. and MaleBS D are highlighted across all subplots. All values are reported as percentages.
To measure 1 we compute a language modeling score L.S based on MLM accuracy for encoder- only models and perplexity for decoder-only models on BIASNEUTRAL, ensuring independence from bias-related terms. For (2). we evaluate a single TPT by summing probabilities of all expected
tokens for each class to approximate P(A) and P(B), and then averaging across multiple TPTs. The
goal is to minimize their difference while enforcing large overall sum due to (3) Multiplying
these scores together yields a Balanced Bias Score (BalancedBS), and the best-scoring configuration
across the parameter grid is selected as the modified model, denoted BaseModel GRADIEND, construct explicitly gender-biased variants to further study
We also use the same framework the capabilities of our approach. A Female Bias Score (FemaleBS) is defined to favor female bias, enforcing high LMS low IP(F), and high P(M). Conversely, Male Bias Score (MaleBS) does the opposite for P(F) and P(M). These metrics yield BaseModel +GRADIEND and BaseModel + +GRADIEND respectively. Precise metric definitions are given in Appendix
While Figure 4 focuses the selected BERT base models for gender, other models show similar overall behavior (see Appendix [F). All selected models for gender, race, and religion are further evaluated for debiasing performance in Section (.4 Interestingly, all plots exhibit nearly point- symmetric behavior. This effect arises from the linear structure of the GRADIEND decoder, which computes dec(h) h Wa f ba. When comparing configurations (h, a) and -a), the resulting difference in weight update is:
base
-a),
[wm m dec(h)] [(".. + (-a) dec(-h)) a (dec(h) + dec(-h))
=a =a[h Wa + bu) + (- h Wa + ba)]
Lith-Wa-s
2aby.
Thus, the only difference is due to the decoder's bias term ba. scaled by 2a. Further, as h increases. the term Wa dominates in the weight update, reducing the relative impact of ba. and thereby enhancing the symmetry. Conversely, the symmetry breaks for small [h] or large |ar)
P(F) and P(M) (Figures 4a and 4bp show an inverse pattern. Due to the encoder normalization and the definition of V_ W m (Section 3.2). when the signs of h and are equal, the model biases consistently toward male, whereas opposite signs bias toward female. LMS Dec (Figure 4 reveals broad region of high probability for moderate learning rates, while Figure 4d illustrates the optimal models for BalancedBS. These plots capture the inherent trade-offs of the debiasing approach (Joniak & Aizawa 2022): stronger bias modification modeling, but safe region exists with moderate feature factors and learning rates. Considering the BalancedBS plot (Figure 4d and feature factor h 0.0, the GRADIEND decoder's bias vector be effectively learned an appropriate debiasing direction. Although not shown in Figure 4 the highlighted selected cells for FemaleBS and MaleBS (see Figure (a) confirm that the method can also enforce strongly female- or male-biase biased models, yielding extreme values of P(F) and I(M).
Specifically,
degrade language
5.4 COMPARISON TO OTHER DEBIASING TECHNIQUES
We compare the SRADIEND-modifiie models alongside up to seven debiasing approaches (see Section (.2). We hypothesize that combining debiasing methods improves debiasing, and for gender, we also evaluate hybrid approaches that pair weight-modifyine methods (CDA, DROPOUT, and GRADDENDDeemlettte with post-processing methods (INLP, SENTDERIAS).
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Table Mean proportional ranks for SS/ SEAT, and mean relative change LMSStereSSe// GLUE SuperGLUE vs. the base model. Models are sorted by the Mean column. AW and PP indicate model weight modification and post-processing, respectively. Best variant type marked with blue V. Variants marked with * use only non-LLaMA models, making absolute language modeling scores less comparable, relative differences (averaged model-wise score difference) remain meaningful.
Variant
Prop. Rank Bias
Language Modeling
Name
W PP
Mean
SEAT
LMSStemonn
GLUE (%)
SuperGLUE
results Tables 1ne
GRADIEND + INLP
0.88
0.91
0.84
87.06
68.25
1:73: 50.65
CDA + INLP
0.75
0.78
0.36
77.55
186 52.67
DROPOUT INIP
/
0.71
0.78
0.64
-1.00
84.42
-2.33
74.75
.0.0 50.01
INLP
X
0.67
0.62
0.72
0.10
87.56
68.83
.082 51.55
GRADIEDDmmeMtee SENTDEBIAS
0.64
0.67
86.34
67.78
51.54
DROPOUT SENTDEBIAS
0.62
0.70
0.55
3.25
82.27
2:25
74.93
0221 50.60
SENTDEBIAS
X
0.60
0.48
0.72
86.94
68.27
.0.0% 52.29
CDA + SENTDEBIAS
0.57
0.71
0.43
85.52
77.68
1.25 52.06
0.46
0.50
0.73
86.72
0,00 68.70
0033 51.73
CDA
V
0.44
0.42
0.45
0.21
85.74
0:45
77.64
1:30 52.18
FEBBASS
X
0.41
0.41
77.81
00.99
0.01
1.7 50.66
GRADIENDEauak
V 0.36
0.51
0.21
+0.79
86.71
4009
68.61
0.41: 52.78
GRADIENDMal
0.32
0.19
0.44
87.13
69
52.02
2.19
@ .0.00
-185
DROPOUT
V 0.30
0.40
0.20
2.11
83.40
3,09
74.10
0:42 50.39
Base Model
X 0.17
0.11
0.23
87.46
68.70
52.37
Race (full results in Table 16
SELFDEBIAS
0.87
0.87
1.24
86.22
GRADIENDA
/ 0.58
0.79
0.36
82.00
22:06 65.94
49.98
SENTDEBIAS
X 0.55
0.49
0.61
87.40
68.31
52.53
DROPOUT
V
0.54
0.57
2.11 83.40
4 3.09
74.10
1142
50.39
INLP
X
V
0.46
0.29
0.64
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87,39
0:35
69.03
0.13
52.50
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0.44
0.25
0.63
83.91
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-8.14
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X 0.44
0.24
0.64
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0.36
0.32
0.40
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Religion (full results in Table 17
SELIDEBIAS
0.70
0.70
-9.80
77.86
SENTDERIAS
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0.64
0.65
0.62
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0.58
0.33
0.83
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0.54
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68.45
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0.59
0.25
0.78
86,68
0.39
69.09
0.14 52.51
Base Model
X
0.33
0.24
0.42
87.46
68.70
52.37
We evaluate on two established bias metrics: SS (Nadeem et al. 2021). which compares stereotypical and anti-stercotypical predictions, and SEAT (May et al. 2019). comparing embedding associations between bias attributes and stercotypical terms. Both detailed in Appendix C.5 As debiasing can harm language modeling (Joniak & Aizawa 2022), we report Language Modeling Score (LMSS (Nadeem al. 202T) capturing language modeling without fine-tuning, alongside the established NLP benchmarks GLUE (Wang et al.) 2018) and SuperGLUE (Wang et al.) 2019).
Detailed results per base model, including bootstrapping intervals (Davison & Hinkley 1997). be found in Appendix G As noted in prior work (Meade et al. 2022), comparing debiasing approaches challenging due sometimes inconsistent performance
models and metries. To address this, we compute an aggregated debias score by ranking each approach based on its proportional rank in SS and SEAT averaged across all seven base models. Table 2 reports these ranks alongside average changes in the language modeling metrics relative to the original model.
5.4.1 GENDER DEBIASING
Among the single approaches, GRADIEND (9th) is the most effective weight-modiffing (AW) approach. Notably, such weight-modified models can be integrated into standard downstream
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implementations, unlike post-processing (PP) methods, which, despite generally stronger perfor- mance (e.g., INLP, 4th), require customized handling. The best overall results are achieved by combinations, with GerattEN INLP clearly outperforming all other methods, followed by GRRADIENDremmmeeMdle + SENTDEBIAS, This supports the intuition that combining debiasing tech- niques can enhance the debiasing effectiveness of individual methods. Nevertheless, strong single approaches like SENTDEBIAS still outperform some combinations.
GRADIENDFemale and GRADIENDMale are designed be female and male-biased models, yet their performance is only slightly below GRADIENDFemleMee and comparable to SELFDEBIAS. We confirmed that all three GRADIEND variants align with their intended behaviors in some examples (see Appendix D.. Notably. the base models themselves are ranked last with a notable gap, i.c., each debiasing approach leads to an actual less biased model according to the utilized debiasing metrics.
5.4.2 RACE AND RELIGION DEBIASING
Debiasing race and religion substantially harder than gender. Base models achieve high proportional ranks, and most techniques yield only marginal or even bias-strengthening effects. In particular, no method yields statistically significant SEAT improvements, and for race, the base model outperforms all debiasing methods on average. SELFDEBIAS performs best overall for race and religion, but is evaluated only on the apparently easier SS metric and with degraded language modeling for religion. Weiiht-modification methods like GRADIEND AsianWhiee and DROPOUT improve bias metrics but degrade language modeling performance.
Although GRADIEND does not achieve top scores in aggregated proportional ranks, it is the only weight- modiffitddiii method with statistically significant improvements for race and religion, while not significantly harming language modeling for some specific models, e.g., GPT-2 +GRADIEND, Asiam/Bllack and RoBERTa + +GR DERRADENDeiistaaMMtuee single bias (e.g., GRADIENDBlas//Wn does not target Asian), full debiasing cannot be expected. Considering that we also did not control the data as carefully (see Appendix [B.4] as for gender (e.g., controlling for other word meanings like the name Christian vs. the religion Christian the actual color vs. race associated terms), this explains the differences to the better performance at the gender debiasing. Thus, without strict controls for training data, GRADIEND still reliable for the identification of features, but we suggest strong controls when models should be rewritten.
(see Appendix G. Moreover, since GRADIEND only targets
5.4.3 OVERALI. RESULTS
Across all bias types, LMSDee generally declines under debiasing, but fine-tuned performance on GLUE and SuperGLUE often remains stable. No method fully eliminates bias across metrics, underscoring the difficulty of the task.
The GRADIEND decoder effectively modify bias (hypothesis (H2) SoTA performance among modifiiadiiiitionn methods. For race and religion. weaker averaged results likely stem from noisier training data and the restriction to a single debiasing axis.
For gender, it achieves
6 LIMITATIONS AND OPEN QUESTIONS
While we have demonstrated GRADIEND's 's effectiveness as a proof of concept for learning bias- related features and modifying model behavior, our study has focused primarily on pairs of orthogonal feature classes. Studying how model can be debiased along multiple axes simultaneously is a natural next step, either by iterative training of partial debiased models along orthogonal axes or combined multidimensional GRADIEND training. Furthermore, using multiple feature neurons even for a single axis could improve debiasing, and may limit expressivity. In addition, it is unclear how well the method generalizes to continuous features, such sentiment scores. Moreover, the current framework should be extended to support multi-token targets for CLM (Appendix [D3). e.g., by iteratively computing single-token gradients for each token individually and averaging them to derive inputs for GRADIEND.
single feature neuron enforces strong compression

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Beyond these technical constraints, questions remain regarding interpretability. For example, compar- ing the most relevant bias neurons across all race and religion gradients, or conducting neuron-level analyses in multilingual settings could reveal deeper insights into internal model representations.
ETHICAL STATEMENT
Our study explores both debiasing and deliberate amplification of binary gender associations in lan- guage models, which while valuable for analysis poses risks if misapplied to reinforce stereotypes. We emphasize that the considered bias classes are simplifications chosen for methodological clarity and do not reflect the full diversity and complexity of gender, race, religion in society.
CONCLUSION
We present a novel approach that achieves two key objectives: (1) learning feature for the desired interpretation along an orthogonal axis based on model gradients, and (2) implementing debiasing technique reduce feature-related bias in transformer language models. contrast to most existing debiasing methods, our approach allows for modifying an already trained, biased model to create truly less biased version. This approach is built on simple encoder-decoder architecture, GRADIEND, featuring single hidden neuron. The model learns to encode feature in an unsupervised manner, using gradients from a specific token prediction training task. We successfully applied this method to various transformer model architectures, showing its wide applicability. The code is publicly available at /hhttp:://gtt tttp.ccmt.t...cooo/ooomm a aieng- lab/ perrdiiendbbias
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A STRUCTURE OF THE APPENDIX
We structure the appendix similar the main part of the paper. This section provides an overview and highlights the most important results complementary to the main part of the paper.
The appendix follows the structure the main paper and provides complementary details and results. Appendix B describes the generated datasets, and Appendix [ defines the evaluation metrics in detail. Training and implementation details are given in Appendix [D] Appendix D presents the complementary plots to Figure 3 showing the distribution of encoded values for all base models (Figure 5). Additionally, we provide an analysis of the stability of the encoded feature neuron across training runs well as brief evaluation of how the encoder generalizes to unseen data and additional gendered target tokens. Appendix provides the corresponding heatmaps to Figure 4 for the selected models (Figures 15) including precise metric definitions and their scores for the selected models. Raw results for Table are reported in Appendix G Appendix [H presents an ablation study on how GRADIEND can be integrated with a fine-tuning task. Appendix [ examines generalization of GRADIEND's 's debiasing effect to unseen tokens. Finally, Appendix J concludes with example
predictions illustrating the impact of gender debiasing.
B DATA
We publish all of our introduced datasets on Hugging Face, Table 4 Details regarding the data generation can be found in the subsequent sections.
For brevity, the term pronouns used to refer specifically to third-person singular gendered pronouns (i.e., "he" and "she"), and name refers exclusively to first names.
B. NAMEXACT
Several datasets were constructed with the help of an existing dataset (UCI 2020), which contains 133,910 names with associated genders, counts, and probabilities derived from government data in the US, UK, Canada, and Australia. From this dataset, we derive two subsets based on name ambiguity: NAMEXACT and NAMEXTEND.
We refer to NAMEXACT and that have no ambiguous meanings, therefore being exact with respect to both gender and meaning. First, we filter all names of the raw dataset to retain only names with count of at least 20,000, resulting in selection of the most common 1,697 names. Next, we remove names with ambiguous gender, such as Skyler, Sidney, and Billie, which were identified by having counts for both genders in the filtered dataset, removing 67 additional names.
collection of names that are exclusively associated with a single gender
To further refine our selection of the remaining 1,630 names, we manually checked each remaining name for ambiguous meanings. For instance, names like Christian (believer in Christianity), Drew (the simple past of the verb to draw), Florence (an Italian city), April (month), Henry (the SI unit of inductance), and Mercedes (a car brand). This exclusion process performed without considering casing to ensure applicability to non-cased models. The filtering resulted in the exclusion of 232 names, leaving us with total of 1,398 names in NAMEXACT
We split the data into training (85%), validation (5%), and test (10%) subsets, ensuring that the latter two splits are balanced with respect gender.
B.2 NAMEXTEND
We define NAMEXTEND as a dataset that extends beyond the constraints NAMEXACT by including words that can be used as names, but not exclusively names in every context.
To limit the number of names while ensuring sufficient representations, we set a minimum count threshold of 100 for the name dataset. This threshold reduces the total number of names by 72%, from 133,910 to 37, 37,425, helping to save computationally time. This dataset includes names with multiple meanings and gender associations, the threshold is the only filtering criterion applied.
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Table Overview generated datasets including total number of samples and description.
Name
Size
Description
NAMEXACT NAMEXTEND
1.39%
Names that are unanbiguous (etaet) in meaniig and gender, Alice, Bob, Eve
40,351
Extends NAMEXACT with less cortain names, including those with multiple meanings genders, c.g., Alice, Bob, Christion, Drew: Eve, Florence, Skyler
GENTER/ Gender T
27,031
Name-gender templates, e.g., [NAME] explained the vision as best (PRONOUN) could.
(A.).
(B.).
Race templates, e.g. Ronks the [MASK] Sudola Championship (ASC)
20.152 (W.
Religion
19,653 (C.), (J).
Religion templates, e.g. Cathedrals of the Roman Catholic [MASK] in Switzerland
4,043 (M.)
BIASNEUTRAL
20,057,351
Contains only bias-ocutral words, e.g., really want to see you again, soon if you can Sennder-tterottpiice templates. e.g., My friend, [NAME], tuking of babies. English Wikipedia templates with diverse masked gendered terms (c man, daughter).
GENTYPES
WIRIGENDER
10,000
Table 4: Hugging Face identifiers of our datasets.
Name
Hugging Face Identifier
NAMEXACT
sieng lab nnmmxxcct
NAMEXTEND
3-eng-
Lab/ hanhnatttt ond
GENTER/ Gender T
Race T
3-er
a357 3479gadieel race cald
Religion T
1ab/ aabggaadion rallg1on 1g0n
BIASNEUTRAL.
abbbbaannutrr
GENTYPES
Bleng
Jab/ htt/geeeEvee
WIKIÕENDER
Sceng
grediend wiki gender
Therefore, names that can be used for both genders are listed twice in this dataset, once for each gender. define the
By considering the counts of how often probability that a name is used for a specific gender. For a given name N and gender F (female) or M (male), we denote this probability as P(F and PNM For example, for the name N Skyler, the dataset contains the probabilities PP(F Skyler) = 37.3% and (PM (K)lly))r) = 62.7%.
is associated with particular gender,
B.3 TRAINING DATA FOR GENDER (GENTER)
For the training of GRADIEND, introduce new dataset called GEnder Name TEmplates with pRonouns (GENTER), which consists of template sentences capable of encoding factual and counter- factual gender information, as illustrated in the motivating example in Section 3.1 Each entry in the dataset includes two template keys: name [NAME] and a pronoun [PRONOUN] For instance, the earlier discussed example sentences can be instantiated from the following template:
[NAME] explained the vision best [PRONOUN] could.
Using the popular BookCorpus (Zhu et al. 2015) dataset, we generated such template sentences that meet the following criteria:
Each sentence contains at least 50 characters.
Exactly name from NAMEXACT is contained, ensuring a correct name match.
No other names from NAMEXTEND are included, ensuring that only a single name appears in the sentence.
The correct name's gender-specific third-person pronoun (he or she) is included at least once.
All occurrences of the pronoun appear after the name in the sentence.
The counterfactual pronoun does not appear in the sentence.
The sentence excludes gender-specific reflexive pronouns (herself, himself) and possessive pronouns (her, his, hers, him).
Gendered nouns (e.g., actor, actress, ...) are excluded, based on gendered gendered-word datase which expanded with plural forms using the Python library inflect resulting 2,42T entries.
https://gi http://hw.....c/mmmm///////neeea
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This approach generated total of 83,772 sentences. To further enhance data quality, we employed a simple BERT model -bert-baseuurd
as a judge model. This model must predict the correct pronoun for selected names with high certainty, otherwise, sentences may contain noise or ambiguous terms not caught by the initial filtering. Specifically, we used 50 female and 50 male names from NAMEXACT and a correct prediction means the correct pronoun token is predicted as the token with the highest probability in the MLM task. Only sentences for which the judge model correctly predicts the pronoun for every test case were retained, resulting in a total of 27, 031 unique sentences. We split the data into training (87.5%), validation (2.5%), and test (10%) subsets. The validation split rather small, due to the large input size of the GRADIEND models (comparable to the size of the base model), see Section 5.1 for more information.
The GENTER dataset specifically designed to train our proposed GRADIEND models, focusing on gradient updates that influence gender-changing directions. The applied filtering constraints ensure that the only distinguishing gender-related factor between the factual and counterfactual versions of sentence is the pronoun (he or she) associated with the actual gender linked to the name. While our experiments show that using the name-pronoun associations in GENTER effectively uncovers a proper feature encoding and debiasing. future work could investigate whether incorporating additional context, such as gendered nouns or adjectives, provides further useful information.
We selected the BookCorpus (Zhu al. 2015) as the foundational dataset due to its focus fictional narratives where characters are often referred to by their first names. In contrast, the English Wikipedia (Wikimedia Foundation (023), also commonly used for the training of transformer models (Devlin et al. 2018 Liu et al. (201)), was less suitable for our purposes. For instance, sentences like [NAME] Jackson was a musician, [PRONOUN] was a great singer complicate bias detection based on first names (as done for GENTER) due to the context of well-known individuals, where the name and pronoun association can be highly influenced by prior knowledge rather than bias.
B.4 TRAINING DATA FOR RACE AND RELIGION
We filter the same Wikipedia dump used by (Meade et al. 2022) to create the templated GRADIEND training datasets for race and religion, similar to how they augmented counterfactual data for their CDA training. Following their approach, we use their defined bias attribute words to identify factual and counterfactual terms. These words consist of triples representing each feature class class, e.g., for Christian/lewish/Muuss
or Asia/Afrric//nrrop for Asian/Black/Whan For each directed pair of classes (e.g., A = Asian and = Black), we retain only sentences containing bias word from A (factual term) and use the corresponding term for B of the triple as counterfactual term. The casing of the counterfactual term matches that of the original factual term (lowercase by default, all caps/ capitalized first letter). The resulting datasets T in training (70%), validation (20%), and test (10%) subsets.
ChurchhSynagogge//MMosp
B.5 BIASNEUTRAL
To evaluate our models on data that is entirely birs-unrelated, we created separate dataset called BI BIASTEU,A,L, again sourced from BookCorpus (Zhu et al.] 2015). Similar to GENTER, we removed sentences containing gender-specific words, as well as any bias attribute words (i.e., the target tokens defined by Meade et al. (2022), such African, Christian, ...). We also excluded sentences containing any name from NAMEXTEND and sentences shorter than 50 characters. This filtering process resulted in total of 20M neas-neutral sentences, with all duplicate sentences removed.
B.6 GENTYPES
Given that the training of the gender GRADIEND specifically involves the relationship between names and their associated genders, we aim to evaluate our models against the gender associations of names with gender-stereotypical contexts select gender bias-changed models.
My friend, [NAME], loves taking care of babies.
My friend, [NAME], is probably into video games.
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We expect gender-biased models assign higher probabilities to female in the first sentence and male names in the second one due to existing stereotypes in real-world data. We generated 500 sentences with GPT-4o (OpenAl 2024) and refer to this gender-stereoypp data GENTYPES.
The dataset was generated with the following prompt:
Generate a copyable CSV dataset with 500 sentences that demonstrate gender bias using the placeholder [NAME] for a name. The dataset should include four columns: "text," " "gender," " and "reason,". The "gender" column should contain "M' for male FF for female. The "reason" column should specify one of the following categories for the gender bias: "Profession," "Stereotype. " "Hobbies," " "Behavioral Traits," " "Skills," "Interests," " "Roles in Family," "Physical Abilities," " "Social Roles," or "Preferences," ". Provide at least 50 sentences for each reason category. Ensure the sentences are diverse and avoid repetitions. Do not use any words that indicate the gender such as "him", "his", "actress"...
The csv file should look like the following:
text.gender.reason
"[NAME] is an outstanding pilot with thousands of flight hours." "Morss....Prroeessss P.o.fsssssioon
"[NAME] fantastic hair stylist with many loyal clients." "ffents.,,F,Prffesssiii "FFProffsssoon
"[NAME] is probably into video games.".M,Innn I,,,rnteests
"[NAME] is likely to be more empathetic. "erpatthttic....Beaavirrrr ,F,Behhavooral Traits
As long as the total number of generated entries were below 500, the dataset was iteratively expanded by repeatedly prompting GPT-4o with "More". All generated entries manually validated to ensure that no gender-specio pronouns (e.g., he, she, his, etc.) were present. Entries containing such pronouns excluded. The final dataset size was capped 500 entries.
Although the gender and reason columns were not directly used in this study, their inclusion was intended enforce balance between male- and female-associated stereotypes and enhance diversity in stereotype contexts. However, this goal may not have been fully achieved, as RoBERTa demonstrates female bias in predictions (see Section 5.3). in contrast to our expectations of generally male biased model.
To encourage the model predict names on these masked sentences, we used the prefix "My friend, [MASK], has a... "rather than "[MASK] has a... which could logically allow for other (unwanted) tokens, such he or she.
B.7 WIKIGENDER
To evaluate how well the GRADIEND encoder generalizes unseen tokens and to data from different source than seed during training, we derive masked texts from the English Wikipedia (Wikimedia Foundation 2023). We filter and mask occurrences of the following gendered target word pairs: shelhe, woman/man. girllboy, motherifather, and daughter/son. For each target, we retain 1,000 texts, forming the dataset WIKIGENDER.
Unlike BookCorpus, the base dataset for GENTER used to train the gender GRADIENDS, Wikipedia articles are much longer (on average R 400 words for WIKIGENDER vs. R 17 words for GENTER), contain structural elements such as headings and newlines, and cover encyclopedic content rather than narrative text. This enables evaluation of both input distribution and target token shifts.
C METRICS
In this section, define the metrics of Section used evaluate the GRADIEND encoder and to select bias-changed models formally and more detailed. Additionally, we discuss established techniques measure bias in language models.
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C.I LANGUAGE MODELING SCORE OF THE DECODER
We use LMSDee as measure of the general language modeling capabilities of a model that may have been modified by the GRADIEND decoder. To ensure that the evaluation is independent of any gender bias change, we employ a TPT on BIASNEUTRAL.
For encoder-only models, the TPT corresponds samples are used for gender evaluation and 1,000 samples for race and religion, reflecting the larger number of GRADIEND models in the latter case. Approximately 15% of the tokens are masked, following standard practice (Devlin et al. 2018), and LMS is computed as the accuracy on the MLM task.
MLM task, where 10,000 BIASNEUTRAL
For decoder-only models, we compute perplexity over 1,000 samples fewer than in the MLM setting, as the model predicts every token in each sequence, resulting in both higher computational cost and more relevant tokens per sample. Perplexity measures the model's confidence, with lower values indicating better performance, ranging from to infinity. To align its interpretation with accuracy, we convert it to LMSDee T+perplezityy yielding scores in [0, 1].
C.2 GENDER PREDICTION PROBABILITIES
This section introduces probabilities for feature class A, P(A). We initially restrict this gender (i.e., female and male probabilities, F(F) and P(M)) for clarity, which are generalized to other feature classes in the following section.
Let A denote the set of single-token names in NAMEXTEND, and let G E (P, M) be a gender. Let the GENTYPES data be denoted as T', i.e., stereotyped sentences with name placeholder [NAME]. e.g.:
My friend, [NAME], loves taking care of babies.
Let / E T be text and [7| denote the number of elements in the set T.
Each text creates TPT f where the goal is to predict a name. For encoder-only models, [NAME] is simply replaced by [MASK], creating MLM task. For GPT-2 and LLaMA, we transform into sentence that naturally prompts name prediction, using the following template style:
The person, who loves taking care of babies, has the first name [MASK]
This is done by removing the prefix "My friend, [NAME]," and the final punctuation from t. For InsMu-Instruct, we use the original text with the [NAME] placeholder as user prompt and prepend system prompt instructing the model to predict suitable name:
You are a language model trained to predict first names. In the following text, [NAME] represents a placeholder for a first name. Your task is to predict the most likely name that fits the context. Return only the predicted name no punctuation, no quotation marks, and no explanations.
The probability distribution over the first generated token is then treated as the model's prediction for similar to the other models.
The probability of predicting name N E N for i is denoted as P.(N). Names are treated independent of casing and leading white spaces, i.e., the probabilities of all such tokens contribute to this probability.
The probability of predicting gender for & is estimated by summing P.(N) for all names N of that gender:
Pr(
Σ (((N)-((GG) E [0, NEN
As introduced Section B.2 IPG represents the likelihood of a name N being associated with gender G. This conditional probability acts filter in the over all names in N. ensuring that names of the other gender do not contribute to the aggregated probability of G. Moreover, P(G[N) ensures that names applicable to both genders contribute only partially to the aggregated probability
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of gender G. For example, for N Skyler, P((Syyler) contributes P(F Skyler) 37.7% to the female probability (F) and (PMM)) = 62.7% to the male probability Pt(M)
The combined probabilities for either male female names is given by
P2(FU U M) = (F) +P E [0,
This probability quantifies the proportion of meaningful predictions for i. The probability of gender G, denoted P(G), averages (G) over all E T, i.e.:
P(G) = T
(() E [0,
ET
C.3 GENERALIZATION OF GENDER PROBABILITIES TO FEATURE CLASS PROBABILITIES
We generalize gender probability framework to other feature classes, such as race and religion, by the following adaptions:
Instead of gender G, we consider general feature classes F, F,F,, F2 E {Asian, Black, White, Christian, Jewish, Muslim].
Instead of GENTYPES
use the test split of T as T.
Instead of names, we use the set of bias attribute terms Ap (Meade et al., (022) for each
feature class as target tokens, i.e., the sets Assion ABlack U AWhite and Achristion U analogous to the name token set / for gender.
Jemish U A Auslim
The conditional probability P(F) A) for a bias attribute term A is defined as Af Ap and otherwise, reducing Equation 2 to (F) EAEAp Pc(A).
These adaptions yield similar definitions for P. (FFUFF))) and P(G).
For encoder-only models, multi-token target terms are handled by computing the joint probability across all tokens, allowing both single- and multi-token bias attribute terms to contribute meaningfully to the per-example probabilities.
For decoder-only models, considering only the first token of each target since it may consist of just one or two characters (especially for the large LLaMA tokenizer) and be poorly aligned with the intended term meaning. Instead, we include all first tokens of the target terms that constitute at least half of the attribute term (in characters), providing a more reliable estimate of the term's 's probability.
can be noisy,
For LLLMA-
the prompt in training, without the special prompt used for gender names (see Section D.3).
C.4 MODEL SELECTION METRICS
The Balanced Bias Score (BalancedBS) integrates the previous measures aiming to quantify how debiased model is over feature classes A and B, by averaging over all texts i E T:
BalancedBS LMS
(P-A) (B))) P.(AUB)) [0.
T
Here, LMSDee ensures that high values indicate models with good language modeling capabilities. The first part of the product in the sum (1 - [F)( (1-IPr(A))
- P. (B)) ) is large if the predictions are unbiased over the two classes A and B, since (4) must be similar TP, (PB) to achieve good score. The second part (I+ (A U B)) ensures that both class probabilities are large to avoid a good scoring of models that assign probabilities close to zero to the class target tokens. A high value BalancedBS indicates relatively debiased model, that has still good language modeling capabilities due to the influence LMS
The Female Bias Score (FemaleBS) measures bias towards the female gender
LMS
FemaleBS
P.(M)) -P(() E [0, 1].
1ET
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Table Hugging Face model checkpoints used in this study.
Model
Checkpoint
Reference
BERT base
bert pert-base-caacm
Devlin et al.
2018
BERT large
bert www.e--geeco
Devlin
2018
DistiBERT
dist pastilbert-bon
Sanh al.
(2019)
RoBERTa
roberta- Large
ct al
2019)
GPT-2
2pt2
Radford al.
(2019b
LLaMA
Grattation
2024
LLaMA-Instruc
meta meta-1lana/ 3aa.aaaaaaaa.2----- Instruct
Grattation et
2024
LMSDee ensures again good language modeling capabilities, (MM) prefers small male probabil- ities, and (F(F') prefers large female probabilities.
Analogously, the Male Bias Score (MaleBS) measures bias towards the male gender:
LMS
MaleBS =
((I-P(()) P((M) e [0.
ET
C.5
BIAS METRICS
Various methods exist in literature quantify bias in language models (see, c.g., Li et al. (2023)
Here, we present a few representative techniques commonly used to measure stereotypical bias.
The Sentence Encoder Association Test (SEAT; May et al. 2019) extends the Word Embedding Association Test (WEAT: Caliskan et al. (017) by using sentence templates to evaluate social biases in encoder models. It compares association strengths between embeddings of predefined attribute (e.g., gender-specific names) and target sets (e.g., stereotypical professions) using cosine similarity. effect size, where larger values indicate stronger bias.
Bias is expressed
StereoSet (Nadeem et al., 2021) is benchmark dataset with context-rich sentences for intrasentence and intersentence tasks. This study focuses on the intrasentence task, where sentence (e.g.. Girls tend to be more [MASK] than boys) requires the model to predict the masked word from three options: stereotypical (c.g.. soft). anti-sterotypical (e.g., determined), and meaningless (e.g.. fish). Two metrics are considered: 1) LMS which measures the proportion of meaningful (stereotypical or anti-stereotypical) options chosen over meaningless ones, reflecting the model's language understanding. 2) SS, which quantifies bias the proportion of stereotypical options selected over antiisttreooyppcal ones. A balanced model achieves 50%.
CrowS (Crowdsourced Stereotype Pairs; Nangia et al. (0200 is a crowdsourced dataset consisting of pairs of sentences: one expressing stereotype (e.g., Woman don't know how to drive), and the other its anti-stereotypical counterpart (e.g., Man know how to drive). A bias score is computed considering the model's preference for one sentence over the other, similar to SS. However, CrowS has been criticized for unreliable bias measurement, including spurious correlations and flawed assumptions about social categories (Blodgett et al., (022). Therefore, we did not use this metric in this study, but report it here for completeness.
Li et al. (2021) analyze the attention associations between gendered pronouns (e.g., she) and oc- cupations (e.g., nurse) in transformer models, using gender-swapped sentences (e.g., replace he by she). The attention scores between the gender-swapped pronouns and the occupation are then compared to identify gender bias attention head level. However, the approach does compute model-specific, aggregated bias score usable for comparison.
D TRAINING AND IMPLEMENTATION DETAILS
Table 5 summarizes the Hugging Face model checkpoints used in our experiments, while Table lists the hyperparameters used for training the GRADIEND models.
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Table 6: Training hyperparameters.
Hyperparameter
Value
Optimizer
Adam
Learning Rate
10 (LLaMA, LLaMA-Instruct;; 10-3 (others)
Weight Decay
x 10-2
Batch Size Gradient Computation
32
Batch Size GRADIEND
1
Training Criterion
MSE
Training Steps
23,653 (Gender); 2,500 (Race, Religion)
Evaluation Steps
250
Evaluation Criterion
Coryr on validation split
D.1 ENVIRONMENT
The implementation is based on Python 3.9.19, and we made the training framework publicly available: httpps://// http:::://tttuu.oml/ http//iinggaaaariiennnttette
The LLaMA-based GRA- DIEND models were trained using three NVIDIA A100 GPUs, while all others used single A100. Each A 100 provides 80 GB of GPU memory, and the system had 504 GB of RAM. The same setup also used for evaluation.
D.2 TOKEN PREDICTION TASK FOR ENCODER-ONE MODELS
The training task for GRADIENDiS motivated as a MLM Devlin et al. (2018) task (see Section 3.I). where the masked token sensitive to an involved feature class, For multi-token targets, insert one [MASK] token per target token in the template text. The MLM loss then naturally aggregates over target tokens, so the resulting gradients reflect contributions from each token.
D.3 TOKEN PREDICTION TASK FOR DECODER- -ONLY MODELS
For causal models, MLM instances are converted into a CLM Radford al. (019a) task by providing only the prefix up to the (first) masked token and predicting the next token at the end of the sequence.
For LLaMA- we use the following system prompt allign its behavior with non-instruction- tuned models:
You are language model that completes sentences. Predict the next word that naturally follows the given text. Return only that word - no punctuation, no quotes, and no explanations.
This prompt is used for all applications of LLaMA-Instruct in this study unless stated otherwise.
Although this modification is straightforward, it is effective only when the target terms can be tokenized single tokens when the primary semantic content is largely captured by the first token (e.g., similar to Appendix C.3). This limitation is particularly noticeable for LLaMA-base models with race and religion terms, illustrated in Figure Future work should investigate methods to handle multi-token targets in decoder-only GRADIEND models.
D.4 CUSTOM INITIALIZATION
Our training setup involves a custom random initialization for the GRADIEND models. The default initialization in Poroh applies a uniform distribution from (=1
Va vm where n is the dimension of the input layer. However, for the decoder, the input dimension is 1, resulting in uniform distribution over the interval (-1, 1). This leads to relatively high absolute initial values compared to the same
the target values, the decoder inputs are typically close to 11. To address this, n for the initialization as for the encoder, which corresponds to the number of used weights in the designated model. Our experiments show that this custom initialization improves training results.
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Table 7: Stability analysis of encoded values across three GRADIEND training runs for gender.
Corgac
Mean Absolute Difftrence of Encoded Values J
Model
Run
Run
Run 3
Mran
Runs 1-2
Rans
Runs
Mean
BERT boor
0.706
0.498
0
BEXT
0.621
0.622
31.60
0.635
0.005
0.177
0.368
0.117
DistiBERT
0,939
0.550
0.887
0.035
0.245
0.256
0.179
RoBERTa
0.965
GPT-2
0.984
0.985
0.984
0.984
0.007
0
0.009
0.006
LLSMA
0
0.983
0.983
0.982
0,005
0,003
0.002
0,004
LLaMM--Intrrcc
0,977
0.976
0.977
0.976
0,005
0.000
0.003
0,004
D.5 TRAINING PROCEDURE
Each training step involves two forward and backward passes through the base model to compute the input and output tensors for the GRADIEND model. For race and religion, the training data for classes A and B is derived by combining the datasets for each source class and augmenting the targets with all valid terms from the other class within the same bias attribute group. For gender, each entry of GENTER is augmented batch-size many times with a name of NAMEXACT to generate the actual training dataset. Gradients are calculated with respect to the target token, e.g. helshe or He/She, depending on the position of the target token. We only used single token targets for training, i.e., the datasets were filtered to exclude multi-token targets or sources.
We use the validation split of T for evaluation during training, following the same procedure as described to compute Corr (Section 5.2). However, as pre-computing these validation gradients require a substantial amount of storage, we use for the gender GRADIENDS all of the GENTER validation split for the smaller models (BERT DistilBERT, and GPT-2), half of the data for the medium-sizze sized models (BERT and RoBERTa), and only 5% for the LLaMA- Maaaed models due to their large model sizes. This ensures that the gradients required for evaluation fit into the memory during training. For instance, the evaluation data for BERT requires approximately 270 GB. For race and religion, a maximum of 1,000 samples is used, with similar relative reductions based on model size, The training time for single gender GRADIEND model ranges from 3.5 hours for DistilBERT to 24 hours for -nnttructt
To monitor progress, the model is evaluated every 250 training steps using Cory, and select the best model after finishing all training steps (Section [5.1). Similar to the procedure to evaluate the GRADIEND encoder (Section 5.2). This evaluation metric focuses on the encoder's ability to differentiate between genders, which measures how well the encoded values distinguish between the feature classes. Notice that this metric evaluates only the encoder, as the decoder's 's role in adjusting bias harder to evaluate.
When training the gender GRADIEND models, they sometimes fail to converge in distinguishing female and male input as 11, depending on the learning rate and random seed. This issue was observed particularly with RoBERTa, although it occasionally occurred with other models as well, depending on the learning rate. In such cases, the first training steps determine whether both genders separated correctly both encoded the same value (either +1 -1). Future research is needed to explore this phenomenon. To mitigate non-convergent runs for gender, we train three GRADIEnD models per base model with different seeds and select the one with the highest Cory on the validation split. For race and religion, a single GRADIEND model is trained per configuration.
E ENCODER AS CLASSIFIER
E DETAILED RESULTS
Similar to Figure we present additional results in Figure showing the distribution of encoded values of race and religion GRADIEND models evaluated against broad set of datasets. The data of these plots has been used to compute Cory and Corene in Table
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Blick
Addeu
tmining
Label
training
Asian/ Blank/Black Black
Asian Whiite
Black/ White
BERT
BERT
DiSiBBRRT
-1.0
RoBERTA -1.-
GPT-2
LLAMA
HaMAA-nstrut
-15-
Astan
Black
White
Neutzal
Asian
Back
White
Neutra
Aman
Black
White
Neutral
(a) Race.
Utristin Jewtsh
Jexlst Cleietinn
Musiim
Chiistian
Tstumaa
Not ned curing traising
Label
Munia
Josisl Mullia
Mullim
BlASoucTnaL
Uno
Christian Jewish
Christian Muristian/Muslin Muslin
Jewish Musliu
BERT
-1.
BERT large -1.0
DisilBERT
-1.
RoBERTa-
GFT-2
+1.
ILMMA
-10
LaMA-Instmatt
LLaMA Instrect
-10
Cheiston
Jewish
Midiim
Nentzal
Chratian
Jeniss
Musliis
Neutsal
Chracian
Jeuish
Muslim
Neutral
(b) Religion.
Figure 5: Distribution of encoded values for all race and religion GRADIEND models across different datasets. The yellow dots indicate the expected label used for Coffee.
24

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Published as a conference paper at ICLR 2026
Rus
Rone
1-2
1-3
E3
Poraa
0.5
0.0
1.0
Maie
0.0
-3.
-0.5
1.0
1.0
Nottal
35
0.5
0.0
DRRT hane
BERTh
DiaBEIT
RoBEITT
GPT-2
IaMM
La MaMt--lsttee Instree
BERT
BERT
DSMBERT
BuBENT
GPT-2
MaMA
LAMA Iostrett
Model
Model
Figure 6: Distribution of encoded values h (left) and their sample-wise difference Ah (right) across
three GRADIEND training runs for gender.
E.2 STARILITY OF ENCODED VALUES
We analyze the stability of the feature neuron by examining the encodings from three independently trained gender GRADIEND models for each base model. Figure O shows the distribution of these encoded values, along with sample-wise differences to highlight run-to-run variation, and Table summarizes key statistics.
With the exception of the BERT-basch models, the feature neuron is generally stable across female, male, and neutral inputs. DistilBERT and RoBERTa show some variability for neutral inputs across runs, while GPT-2, LLaMA, and LLaMA--IsSin exhibit a mean absolute encoding difference below 1%.
For BERT iarges the third run achieves notably higher performance than the first two, which are fairly similar to each other. In contrast, BERT hase shows non-convergent second run, resulting in large differences compared to the other runs.
E.3 GENERALIZATION OF ENCODED VALUES
We further analyze how the encoder generalizes to unseen inputs, considering two aspects: (1) the input sentences originate from dataset different from the used during training, and (2) the evaluation involves gender-related target tokens beyond the training pair he/she. Therefore, we use WIKIGENDER dataset (see Appendix [B7)).
Figure 7 shows the distribution of encoded values for our seven gender GRADIENDS. The she/he encoding learned during the training transfers well to WIKIGENDER, indicating that the feature not tied to the specific structure, linguistic style, and gender-filtered property of GENTER.
For BERTouse iages and DistilBERT, the learned feature also generalizes to other gendered token pairs such as woman/man, though the separation is bit weaker than for she/he, as more samples are falsely encoded as neutral (i.e., around 0.0). plausible explanation is that masking he/she yields highly constrained prediction space, only few tokens fit the syntactic and semantic context, whereas masking, for instance, woman/man allows usually a broader set of contextually plausible alternatives (e.g., girl/boy), including gender neutral terms like person. Interestingly, RoBERTa behaves differently: it appears to encode a narrow she/he-specific feature rather than a broader gender feature.
For decoder-only models, the generalization is weaker for non-she/he pairs but still visible, as the female-associated tokens tend encode larger values than their male counterparts. This less
25

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Published as a conference paper at ICLR 2026
Gender
Female
Made
BERT base
1.0
0.5
0.0
0.5
1.0
BERT lange
Gender
Female
Male
1.0
GPT-2
0.5
1.0
0.0
0.5
0.5
0.0
1.0
0.5
-.0
DistilBERT
LLaMA
1.0
0.5
1.0
0.0
0.5
0.5
1.0
-0.5
1.0
RoBERTa
LLaMA-Instruet
1.0
1.0
0.5
0.5
0.0
0.5
0.5
1.0
1.0
she/he
woman/ man
gid /byy
nother ffather
daughter/san son
she//u
woman man
gil/boy
mother ffather
danghtor/son /son
Token
Token
(a) Encoder-only models.
(b) Decoder-only models.
Figure 7: Distribution of encoded values of gender GRADIENDS for WIKIGENDER.
extreme encoding is expected because these models can only use the left context of the target term. Considering the non-she/he token pairs for GPT-2 and LLaMA, they show mostly symmetric distribution around zero with smaller magnitude than for she/he, indicating weaker separation. In contrast, LLMMA-Insruct still shows female-male distinction, but the distributions are shifted toward the male side (i.e., toward -1).
Overall, the results indicate that the features learned by GRADIEND generalize, but that future work should explore training GRADIENDS using multiple facets, i.e., not only single type of counterfactual (e.g., she/he), but also other in parallel, like woman/man to possibly find more general feature representation.
F DECODER AS BIAS- CAAS-CANGEER
Similar to Figure we present the results for all gender models in Figure We further report the selected race and religion models in Figures 15
Overall, a similar selection is different compared to BERT huse: where FemaleBS and MaleBS exhibit negated feature
pattern can be recognized across all figures. However, the model
26

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Published as a conference paper at ICLR 2026
(a) BERT
Derinn Falro
Lover 3M
LexTin Fare
Conting Rab
Locaa Remt
(b) BERT Juge
(c) DistilBERT
(d) RoBERTa
2:30
2TT
(e) GPT-2
2TT
(1 LLaMA
2 2233
Lanaing 2at
(g) LLLMMt-Intruuct
Figure& 8: Metrics for changed models based on the gender GRADIENDS with varying feature factor and learning rate. The cells with the best BalancedBS D, FemaleBS D, and MaleBS are highlighted across all subplots. All values reported percentages.
27

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Published as a conference paper at ICLR 2026
Learnng Ka0
Leariigg 1ixe
#1Black1
KXMuskn)
Lourning Hara
Leariin 4ara
Louriing 4a3
Learning Rata
LM45
LM4
1M4
[M4
2000033.55 33.33
Learningg 1ave
Learring 1404
Learring 140
Leurring 740
Lnaree 4.0
anccedts
anccdds
Dxtancediis
Datancedits
Datancedits
Bslancodes
SA (JAMM
(a) Black/Asian (b) Asian/White (c) Black/White
(d) Chr./Jew. (e) Chr/Muslim (f) Jew./Muslim
Figure 9: Metrics for changed models based the BERT basc and religion GRADIENDS with varying feature factor and learning rate, The cells with the best BalancedBS are highlighted across all subplots. All values are reported as percentages.
#CChyssan)
ACChnatun
23
J0000 20600-20-0000000 23 2013
23 220 2013
Liavine K.0
Learning Raca
Leuniin 1444
Learring Ras4
Learning Rat4
1145
[M4
[M4
200 50 (11
2030 18 2012
2000 18 23
Learring Tare
Learingg 425
Lerriing 1ute
Learring 1ute
Leariigg Tate
LAMving 4M0
ancedds
2ncodd
Datanced85
Dalanced
Datanced
Balanced85
FAMM
7RZANNI A7AAAAM
(a) Black/Asian (b) Asian/White (c) Black/White
(d) Chr./Jew. (c) Chr/MMulim (f) Jew./Muslim
Figure 10: Metrics for changed models based on the BERT race and religion GRADIENDS with varying feature factor and learning rate. The cells with the best BalancedBS are highlighted across all subplots. All values are reported percentages.
28

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Published as a conference paper at ICLR 2026
PIChiStun)
STChS
PYonisti
Learnng Rales
Loarrin
(2.5fac))
Kata
Learting PaRi
Leariing eatr
Leariig
Learing MM
Learring Tate
LVS5
LMSc
LMS
LHS
1N55
00
Kale
Leareny Ratr
Lniiiing
Leatin MMY
(a) Black/Asian (b) Asian/White (c) Black/White
(d) Chr./Jew.
(e) Chr/MMulim (I) Jew./Mullim
Figure 11: Metrics for changed models based on the DistilBERT race and religion GRADIENDS with varying feature factor and learning rate. The cells with the best BalancedBS D are highlighted across all subplots. All values are reported as percentages.
AVAslan)
Plewish
Learning Pala
Learting Falh
Loariing fate piMus/m))
fate
Learning J4r
Liuriiee 4211
LMS
LMS
20 2013
Learting P331
Learn ng Rale
Leartng A230
Learing Kill
Ralanccdls
Balanced55
BalanceddS
Balancede5
Balanced8S
Leareng
Uerreng Rale
Leareng Rali
Rite
fate
Rate
(a) Black/Asian (b) Asian/White (c) Black/White
(d) Chr./Jew.
(e) Chr./Muslim (l) Jew./Muslim
Figure 12: Metrics for changed models based on the RoBERTa race and religion GRADIENDS with varying feature factor and learning rate. The cells with the best BalancedBS are highlighted across all subplots. All values are reported percentages.
29

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Published as a conference paper at ICLR 2026
Leariin Kali #[WWite)
Learning Fatx
Leaning 800
Laaning Roco
#14y5k0l
Learning Fale
Leariin Fah
Leariin Kall
Leariig Kita
Leatiin kaci
Rani
LVV5
LMS
(MS
UMS
LMS
LMS
Learting
Leariin Falk
Learnin Fale
Learnin Rack
Leaming Rack
Laaning Rack
(a) Black/Asian (b) Asian/White
(c) Black/White (d) Chr./Jew
(c) Chr.MMuslim (f) Jew./Muslim
Metrics for changed models based on the GPT-2 race and religion GRADIENDS with varying feature factor and learning rate. The cells with the best BalancedBS O are highlighted across all subplots. All values are reported as percentages.
Figure 13:
ATChrtttan
ATChnSttan
#tMuckmi
#tMaakmn
Learnny Rate
Led Why Rate
Learting F4lh
Leavine Rali
Leaviin Kuk
Loavino kyn-
LMS
LMS
LMS
LMS
LN
2550013501 13003
2550015001550 121
255001331150 1211
con
200
Leai Kigg Aata
LoM Wig Aata
Liaring Rati
Leavting
Leaving Aib
Laaving Adt
(a) Black/Asian (b) Asian/White (c) Black/White
(d) Chr./Jew
(e) Chr//Mulim (f) Jew./Muslim
Figure 14: Metrics for changed models based on the LLaMA race and religion GRADIENDS with varying feature factor and learning rate. The cells with the best BalancedBS D are highlighted across all subplots. All values are reported as percentages.
30

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Published as a conference paper at ICLR 2026
poRlack
Learnny Rath
Leareny R.M
Leareny Rali
Leariig
Leattoal Kyr
Leararo
L,V5ce
1N5
LN5
UMS
LN5
LM5
Loarnig
Leartin
Learting Palk
Pate
Dabanced555
Dabancedt55
Dabenced555
Datenced555
Dalanced35
(a) Black/Asian (b) Asian/White (c) Black/White
(d) Chr//Jw.
(c) Chr//Mullmm (f) Jew./Muslim
Figure 15: Metrics for changed models based on the LLaMA-Instnus race and religion GRADIENDS with varying feature factor and learning rate. The cells with the best BalancedBS D are highlighted across all subplots. All values are reported as percentages.
Table 8: Selected gender-debiased, female-biased and male-biased models based on BalancedBS, FemaleBS and MaleBS.
Model
FF h
LR
P(F)
P(M)
LMSppe
BalancedBS
FemaleBS
MaleBS
BERT base
0.0
0.0
0.413
0.538
0.496
0.322
0,110
0.172
GRADIENDeemAAMhile
0.0
1c-02
0.424
0.494
0.491
0.363
0.111
0.145
1.0
-1c-02
0.948
0.034
0.490
0.041
0.449
0.001
GRADIENDMal
-1.0
-1c-02
0.031
0.940
0.490
0.043
0.001
0.446
BERTugge
0.0
0.0
0.432
0.534
0,511
0.280
0.132
0.184
GRADDENDgeemLeMiee
-022
Se-02
0.461
0.434
0.486
0.397
0,128
0.115
GRAADENDDemms
0.6
-5e022
0.960
0.031
0.508
0.036
0.473
0.001
+ GRADIENDMak
-0.4
-5e-02
0.024
0.963
0.511
0.031
0.000
0.480
DistiBBERT
0.0
0.0
0.307
0.361
0.392
0,230
0.075
0.096
+ Domm
-1.0
5e-04
0.358
0.320
0.386
0.231
0.092
0.078
GRADIENDEemik
0.8
-1e-22
0.724
0.045
0.350
0.080
0.241
0.004
GRADIENDMal
0.0
-Se-02
0.036
0.599
0.384
0.092
0.005
0.221
RoBERTa
0.0
0.0
0.555
0.404
0.573
0.208
0.249
0.162
GRADIENDeemaAMiie
0.4
1c-01
0.402
0.499
0.560
0.354
0.127
0.181
GRADIENDRemak
0.4
-Se-01
0.980
0.016
0.539
0.019
0.520
0.000
GRADIENDMal
-1.0
-1e-01
0.023
0.966
0,568
0.032
0.001
0.535
GPT-2
0.0
0.0
0.028
0.089
0.107
0.012
0.003
0.009
GRRADIENDremmiMhll
-0.6
1c-01
0.172
0.130
0.106
0.031
0.016
0.011
10.0
-1e-02
0.270
0.061
0.099
0.025
0.025
0.004
GRADIENDMall
-0.4
lc-01
0.123
0.130
0.108
0.027
0.012
0.012
ILAMA
0.0
0.0
0.111
0.162
0.095
0.022
0.009
0.014
0.2
Se-01
0.109
0.174
0.104
0.027
0.009
0.016
GRADIENDEema
-2.0
1e-01
0.194
0.050
0.091
0.019
0.017
0.004
+ GRADIENDMal
2.0
1c-01
0.042
0.271
0.101
0.024
0.003
0.026
ILLMA--Instan
0.0
0.0
0.397
0.399
0.073
0.021
0.025
0.025
+ GRADIEENDemmkMMaee
0.4
Se-01
0.147
0.587
0.086
0.035
0.005
0.043
GRADIENDiemake
-10.0
le-02
0.594
0.257
0.070
0.024
0.036
0.013
GRADIENDDAak
-2.0
-1c-01
0.116
0.569
0.084
0.028
0.006
0.044
31

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Published as a conference paper at ICLR 2026
Table 9: Selected debiased models based on BalancedBS for race and religion. Classes A and refer to the classes of the GRADIEND A/B-
Model
FF h
LR
Base P(A)
P(A)
Base P(B)
P(B)
LMSpec BalancedBS
GRADIEND Asian Black
BE
-0.0
-0.5
8.6c-8
8.6c-8
8.1c-4
8.1c-4
0.587
2.2c-7
BERT harg
1.0
-0.5
3.8c-9
3.8c-9
1.8e-3
1.8e-3
0.635
3.8c-8
DistilBETT
-0.6
1.2e-5
1.1e-3
1.1e-3
0,429
2.7e-6
RoBERTa
-0.0
-0.5
9.6e-7
9.6e-7
1,5e-6
1.5e-6
0.591
7,5e-7
GPT-2
1.0
-0.5
5.7e-7
5.7e-7
1.2e-5
1,2e-5
0.095
2.0c-7
ILaMA
-0.8
-0.5
3.90-5
3.9e-5
2.4e-5
2.4e-5
0.089
4,5e-5
IaMM--nntrrut
-10.0
0.5
7.6e-5
7.6e-5
3.6e-4
3.6e-4
0.068
1.0e-5
GRADIEND Asion Whitee
BERT
-0,2
-0.5
4.7c-8
4.7c-8
3.8c-4
3.8e-4
0.570
7.8c-7
BERT lurge
2.0
-0.5
3.3e-9
3.3e.9
2.2e-4
2.2e-4
0.627
3.0e-8
DistlBERT
0.4
-0.1
8.4e-6
8.4e-6
3.7e-4
3.7e-4
0,421
5.0e-6
RoBERTa
-0.0
-0.5
1.7e-6
1.7e-6
2.5e-6
2.5e-6
0.649
1.4e-6
GPT-2
0.2
0.5
6.1c-7
6.1c-7
1.3c-5
1.3e-5
0.089
2.4c-7
LLaMA
-1.0
-0.5
3.0c-5
3.0c-5
2.20-5
2.2o-5
0.092
4.7c-5
LLaMA-Instmu
-2.0
0.5
8.0c-5
8.0c-5
1.26-3
1,2c-3
0.077
7.6c-6
GRADIEND Black WWite
BERT base
-1.0
9.8e-3
9.8e-3
9.8e-3
9.8e-3
4.2e-3
BERT luge
0.4
-0.5
1.2c-2
1.2o-2
1.2o-2
1.2o-2
0.627
4.1c-3
DistiBBERT
-0.2
0.1
5.4e-3
5.4c-3
5.4e-3
5.4e-3
0.441
1.7c-3
RoBERTa
0.8
-0.5
5.8c-6
5.8c-6
5.8c-6
5.8c-6
0.710
7.7c-6
GPT-2
-0.0
-0.5
1.4e55
1.4c-5
1.4e-5
1.4e-5
0.090
1.1c-6
-0,2
3.0e-5
3.0e-5
3.0e-5
3.0e-5
3.1e-6
LLaMA-Instruct
1.0
0.5
1.1e-
1.1c-3
1.1o-3
1.1e-3
0.067
2.7c-5
GRADIEND GRADDeNDChristien//eeee Christian/JJuuich
BERT base
0.8
0.1
1.5e-2
1.5e-2
2.2e-3
2,2e-3
0.592
3.7e-3
BERT Jarge
1.0
0.5
1.8e-2
1.8c-2
2.6e-3
2.6c-3
0.607
5.0c-3
DistiBBERT
-0.2
0.1
8.4c-3
8,4e-3
6.2c-3
6,2c-3
0.416
1.7c-3
RoBERTa
-0.0
0.5
3.0c-7
3.0c-7
5.7c-10
5.7c-10
0.665
1.4c-9
GPT-2
0.5
5.4e-6
5,4e-6
3.4e-6
3,4e-6
0.091
4,8e-7
LLaMA
-10.0
-0.1
7.9e-5
7.9e-5
1.4-55
1.4e-5
0.085
5
LLaMA-Instruct
-2.0
0.0
4.3e-3
4.3e-3
1.5e-4
1.5e-4
0.075
3.6e-6
GRADIEND GRADDEND Christionnnnnnnnneeoooo MMustim
BERT
-2.0
0.1
1.le2
1.lc-2
1.5e-3
1.5c-3
0.585
3.3c-3
BERT
-0.8
0.5
1.2e-2
1.2e-2
2.0e-3
2.0e-3
0.611
3,6e-3
DistilBERT
-0.0
0.1
7.6c-3
7.6e-3
3.0e-3
3.0e-3
0.430
1.5c-3
RoBERTa
0.8
0.1
4.0e-8
4.0c-8
1.9e-8
1.9e-8
0.664
4.5c-9
GPT-2
-0.8
-0.5
5.4e-6
5.4e-6
2.6e-5
2.6e-5
0.090
8.2e-7
LLaMA
-0.6
-0.5
7.4-5
7.4e-5
3.5e-5
3.5e-5
0.072
3.1e-5
LLMA--Instruc
-0.4
0.5
3.9e-3
3.9e-3
1.5e43
1.5e-3
0.094
9.3e-6
GRADIEND, Jenctsh/Muatte
BERT
0.8
-0.1
1.7e-3
1.7c-3
1.7e-3
1.7e-3
0.589
6,4c-4
BERT turg
0.6
0.5
1.4e-3
1,4e-3
1.4e-3
1.4e-3
0.592
5.7e-4
DistiBERT
-0.4
5.2e-3
5.2e-3
5.2e-3
5.2e-3
0,433
5.7e-4
RoBERTa
-0.2
-0.5
3.4e-10
3,4e-10
3.4e-10
3,4e-10
0.706
4.5e-10
GPT-2
1.0
-0.5
3.4c-6
3,4c-6
3.4c-6
3.4c-6
0.093
1.0c-6
LLaMA
10.0
-0.1
1.3c-5
1.3c-5
1.3c-5
1.3c-5
0.086
1.0c-5
LLaMA-Instruct
0.4
0.5
3.5c-1
3.5c-4
3.5c-4
3.5c-4
0.066
1.0c-5
32

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Published as a conference paper at ICLR 2026
Table 10: Published gender debiased models on Hugging Face.
Model
Identilier
BERT DRRADIENDemmatttee
sieng lab Jab/bert base-aaaeeeeeeeee cased wawe--acaeeedeeeeeeeeee gradiend gender debiased
BERTur DRADDENDFonnsMMe
B Bleng
lab/bert awgge-cceeeee gr gradiend seaaddidd-gdddeee gende:-ceb ceblased
DistiBEET GRADIENDeewd//Mle
Baeng
Leb/disti.bert
cased gradiend gende:
RoBERTaa GRADIENDrwaaMMMe
Baeng
12E LaE/rcserta-ae Largeeraaeeeeeee gradegrdddeeeeee gender-deneeeeee debiases
GPT:2 GRADiENDFanneMMi
D1eng-
LLaMA GRADIEND
1arig-lab//110 lab/Slana-3 geaadgrradl lndd gender deo! dead ased
LLaMA-losruct GRADUNDDemabMti
sieng
lab/llana 3,2 33 Ins Lruct gradiend gender deblaced
factors along with negative learning rates, while BalancedBS features a zero feature factor and a positive learning rate. Similar configurations exist across most models that outperform the base model with respect to BalancedBS, FemaleBS, and MaleBS. The final selected models, however, perform better with respect to metrics, though they do not adhere to the expected pattern. Future research should explore the stability of these parameter choices without relying on a larger search grid.
The statistics for all selected gender models are reported in Table g Interestingly, the difference in BalancedBS between DiRADiENDremaleMiiee and its base model relatively small, whereas the corresponding differences for FemaleBS and MaleBS are much larger, respectively. This obser- vation suggests that biasing model (in either direction) casier than debiasing Notably, Fe- maleBS approaches nearly zero for GRADIENDMale models, and MaleBS similarly is close to zero for GRADIENDReaat models. Surprisingly, for the RoBERTa base model, P(F) P(M) holds true, unlike all other base models. This indicates female bias in the given task, contradicting our expectation that language models typically exhibit male bias (although this bias direction is not captured by SS and SEAT).
The statistics for the selected race and religion models are reported Table
G COMPARISON TO OTHER DEBIASING TECHNIQUES
This section provides supplementary details for Section 5.4 which compares our method to existing debiasing techniques. To facilitate future comparisons with our approach, release our gender- debiased models on Hugging Face (Table 10). where they achieve SoTA debiasing performance.
G.1 IMPLEMENTATION DETAILS
For the evaluation of our gender-changed models GLUE, SuperGLUE, SEAT, SS, and L.S we primarily rely on the bias-bench implementation by Meade et al. (2022). which we also use to compute and evaluate the baseline debiasing techniques: CDA, DROPOUT, INLP, SENTDEBIAS, and SELFDEBIAS. For implementation specifics and metric definitions, we refer the reader to the original work.
Since the original implementation did not include the DistilBERT model, we applied the same hyperparameters for DistiBBERT as for BERT and RoBERTa. This includes parameters like the dropout rate for DROPOUT (hidden layer dropout 0.20 and attention dropout 0.15), and the number of iterations for INLP (n 80). We also adapt this INLP configuration for the LLaMA- based models.
In addition, we integrated RLACE (Ravfogel et al. 2022 and LEACE (Belrose et al. 2023) into bias-bench in analogy to INLP, using their original implementations. For RLACE, we use a rank of 1. We release modified version of bias-bench GitHut
For evaluating the LLaMA-base models on GLUE and SuperGLUE, we use a zero-shot setting based gender-bias adapted version of the Python library ceovaluation-harees (ao et al. 2024). Since STS-B is a regression task, we omit it from the evaluation. For LLaMA-Instrucc we use no system prompt for all of these evaluations. For all non-LLaMA models, follow the standard bias-bench settings and fine-tune them on all nine GLUE and all eight SuperGLUE tasks prior to evaluation.
https:://itthbb.com/
m-ammmmeallllla - -haraarnee
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We exclude GRADIEND and GRADIENDALle from combinations they are not designed for debiasing, and we also exclude RLACE, LEACE, and SELFDEBIAS due to their generally weaker performance. CDA and DROPOUT variants also excluded for LLaMA-base Maadeddd models due to the high cost of additional pretraining for these methods.
G.2 DETAILED RESULTS
We report the raw results for SS, SEAT, Steeeses GLUE, and SuperGLUE in Tables 14 covering BERT buse. BERT DisT RoBERTa, GPT-2, LLaMA, and LLaMA-Instruct. The proportional rank-based comparison in Table ii is derived from these values. Additional sub-results and further information on GLUF and SEAT are provided in the following sections.
The proportional rank (Table 2 for a metric m (SS or SEAT) is derived from Tables to by first ranking the debiasing approaches for each base model. These integers are then converted to proportional ranks by dividing by the number of variants minus one, yielding scores in [0, 1]. This naturally accounts for differences in the number of variants across models. The mean proportional rank for vn obtained by averaging over all base models, and the Mean column in Table reports the average of the mean proportional ranks for SS and SEAT.
The difference values for the language modeling metrics in Table (LMSsterraSer GLUE, SuperGLUE) computed by first taking the score difference for each base model and then averaging these differences across all base models used by variant. Some debiasing variants cannot be applied to all models (all DROPOUT- and CDA-based variants and both LLaMA-based models),
the number of scores entering the average differs across variants. This makes the absolute mean scores not directly comparable for these cases. A reader might expect the reported change to be the difference of averaged scores, but this would not correctly reflect situations where variants use different sets of base models. Reporting the average of model-wise differences ensures that the reported relative changes remain meaningful for assessing whether a variant negatively affects language modeling performance, which is the main concern for this study.
Unlike previous studies, we report all metric scores with a 95% confidence interval, computed via bootstrapping (Davison & Hinkley 1997) from the raw prediction values, providing more robust comparison of model performances. For each score, generate 1,000 bootstrap samples and report both the bootstrap mean and the corresponding 95% confidence interval. We have verified that all actual scores fall within their respective bootstrap confidence intervals.
Statistically significant improvements (i.c., non-overlapping confidence intervals compared to the baseline) are indicated in italics, while the best score for each base model is highlighted in bold. In general, the comparison of debiasing approaches is challenging due to the high uncertainty and variance across different gender-bias metrics. Therefore, we reported the rank-based aggregated score in 2 enable more robust comparisons. Notably, with confidence intervals as context, the effectiveness of existing debiasing methods appears less clear than suggested by prior research (Meade et al. 2022).
G.3 GLUE
For GLUE (Wang et al. 2018). the reported score in Tables 14 and 17 is an aggregate of its subscores. which are detailed in Tables and Due to space constraints, the confidence intervals for individual sub-tasks are not shown per model; however, Table [T presents the confidence margin ranges for each sub-task across all GLUE evaluations of this study. We report the Matthew's 's correlation for CoLA, the Fl score for MRPC, the Spearman correlation for STS-B, and accuracy otherwise. For aggregating the subscores, the MNLI-M and MNLI-MM scores first averaged, and then this intermediate result is combined with the other GLUE subscores.
We follow the same training configurations as Meade al. (2022) though evaluate twice per epoch and select the best performing model based on loss at the end of the three-epoch training.
The reported scores bootstrapped means three runs with different random seeds. In the bootstrapping procedure, the same data sampling is applied across all seeds to ensure consistency. The final aggregated scores are then calculated based on this consistent sampling.
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Table 11:
Minimal and maximal confidence margin of error (in percentages) for GLUE and its subscores, based on the results of Table 18 8 to 21 sorted by number of validation samples.
Task
Min (%)
Max (%)
# Samples
GLUE
1.02
2.00
69,711
QQP
0.23
0.47
40,430
MNLI-MM
0.48
1.02
9,832
MNLI-M
0,46
1,02
9,815
QNLI
0.53
1.40
5,463
1,500
CoLA
0.00
6.27
1,043
SST-2
1,21
3.36
872
MRPC
1.39
5.96
408
RTE
3.18
6.25
WNLI
4.52
11.94
71
Table 12: Minimal and maximal confidence margin of error (in percentages) for SuperGL.UE and its subscores, based on the results of Table 22
25
sorted by number of validation samples.
Task
Min (%)
Max (%)
Samples
SuperGLUE
1.18
2.37
19.293
ReCoRD
0.01
1.03
10,000
MultiRC
0.14
1.63
4,848
BoolQ
1.03
1.64
3,270
WiC
2.00
3.98
638
RTE
3.55
6.04
277
WSC
3.08
9.48
104
COPA
4.36
8.89
100
CB
5.80
14.63
56
Table II highlights the relationship between the number validation samples and the confidence of computed score: tasks with fewer validation samples generally exhibit wider confidence intervals, reflecting greater variability and reduced reliability in their reported scores.
G.4 SUPERGLUE
We compute SuperGLUU (Wang et al., 2019) scores following the same settings as for GLUE, Crucially, the ReCoRD task is modeled as a span-selection problem and MultiRC as a binary sequence- classification problem by pairing each candidate answer with its question. For bootstrapping for these two tasks, examples are always added along with all their associated candidate answers to preserve the task structure..
Sub-scores for SuperGLUE are reported in Tables 22 to 25 As with GLUE, Table summarizes confidence intervals across all evaluated models in this study.
G.5 SEAT
Similar to GLUE, the reported SEAT score in Tables 14 and 15 an aggregated score derived from multiple subscores. We utilize the same sets Meade et al. (2022):
Gender: SEAT-6, SEAT-6b, SEAT-7, SEAT-7b, SEAT-8, and SEAT-8b.
Race: ABW-1, ABW-2, SEAT-3, SEAT-3b, SEAT-4, SEAT-5, SEAT-5b.
Religion: Religion-1, 1, Religion-1b 1b, Religion-2, Religion-2b.
We report the full sub-metric results in Tables 26 to 29 absolute subscore values.
The final SEAT score is the average their
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Table 13: Ablation study of GRADIEND applied at different stages relative to fine-tuning. The Model column indicates the sequence of fine-tuning and GR application. Task accuracy (WSC) and debiasing metrics (SS, SEAT) are reported for each configuration.
Model
(%) 150
SEAT (98)
(S) 1
61.23
68.01
BFRToaee GRAMINNDSeulMMM
..75
14.69
BERT
WSC
9.80 48.57
4.64
61.97
62,50
DERT WSC - GRADIENDRa
46.16
61.41
GRAmINNPguaabMM WSC
-10.3 49.
-1.19 56.42
0.96 63,46
BERT GRADIENDyaalbMbe WSC GRADIENDjaasieMll
66.90
58.94
62.50
BERT
61.23
99.08
BERTarge GILADIINDRarrbbMt
5.58
.2.2
Ba WSC
-7855 46.60
-1.00
57.48
63,46
BERT Dr WSC - GRADIENDyams/Mii
$1.28
62.
63,46
BFRT lans GiRADINNDammaeMa WSC
-1043 50.79
-0.04
lage
FaabiiN WSC GRADIENRRmmMMMe
-10.77 49.34
4.84 63.92
63,46
DisiBERT
84.32
59.25
DiniBERT - GRADIENDDyaskMii
84.27
58.85
1:15
.872
DistiBEXI WSC GRADDEEDDyasbbMil
33.00 $2.52
47.00
63.46
DistiBERT GRADIEEDDeambMMa WSC
(.9% 85.31
-605 6.880
63,46
DistiBEXI GRADiENDFuukMMi WSC GRADIENDsandeMMee
-0002) $4.31
-6900
47.65
63,46
RoBERT:
66.82
62.80
RoBEKIa- GRADIENDRanieMMi
63.92
-12.03
50.77
RoBEK6- WSC
-16 50.33
.3,30
9.50
63,46
RoAFRT: - WSC - GRRAnIETTeessssMtr
1 48.05
52.81
63.46
RoBeKta GRADLENBReeMMMMe
13225
25,96
RoRFRT: GRAMIENT/emdMMM WSC GRABIINPammkMii
( -1669 50.13
<5114
9.66
-2690
36.54
GPT-2
62.65
11.28
GPT-2-- Cminiiiymmmiiitii
3:83
GPT-2-+ WSC
0.14 62,50
5.50
16.84
56.73
GPT-2-+ WSC
99.33
50.42
47.12
-471 57.93
25.55
56
6.73
GPT-2-+ GRADDEENDFunooMMi WSC GRADIENDFonstoMdd:
58.51
32.67
43.96
63.46
H GRADIEND IN COMBINATION WITH FINE- TUNNIG
Table 13
presents an ablation study combining GRADIEND with fine-tuning task: Winograd Schema Challenge (WSC: Levesque et al. 2012) from SuperGLUE (Wang et al. 2019). We report task accuracy alongside debiasing metries SS (Nadeem et al., 2021 and SEAT (May et al. 2019).
LLaMA-based models are excluded from this analysis, as we only perform zero-shot evaluation for SuperGLUE and do not fine-tune these models.
The results show that fine-tuning on WSC alone generally provides a debiasing effect, except for DistiBBRTT and GPT-2. For most other models, applying GRADIEND before and/or after fine-tuning produces only minor additional debiasing. In contrast, DistilBERT and GPT-2 exhibit consistent debiasing effects when GRADIEND applied before and/or after fine-tuning, although GPT-2 demonstrates losing the debiasing effect when fine-tuning follows GRADIEND. Task performance remains unaffected in seven out of cases where GRADIEND the last step.
In summary, applying GRADIEND after fine-tuning ensures the debiasing effect is not overwritten by the fine-tuning process, but can sometimes slightly reduce task performance. Applying GRADIEND before fine-tuning has the advantage that the debiased model can be reused across multiple fine-tuning tasks, requiring only single GRADIEND training and application.
I OVERFITTING ANALYSIS OF GRADIEND
We further investigate whether our approach is prone overfitting, especially regarding the names used (or not used) during the training of the gender GRADIEND models. The previous name- based analysis in Section 5.3 establishes metrics that independent of the data split due to the definition of female and male probabilities.
We consider MLM tasks with opposite orders.
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9,24
Delawin Tap
Ban Soeid
Cuiminu
BERT
RoRFRTA
81 F(eas)
02
Prase]
P(nna)
(a) Is [NAME] "woman" a "mann? [NAME] is "[MASK]".
BIETT
DistBBERT
RoBERTI
P((an)
Pyass]
P(nnn)
(b) Is [NAME] a "man" or "woman" ?? [NAME] is a "IMASKI"
and woman in "Is [NAME] "woman" "man"? [NAME] is "[MASK]"." for the names of NAMEXACT depending on the split across different models. The dashed line represents the identity function.
Figure 16: Average probabilities for predicting
Is [NAME] a "woman" or a "man"? [NAME] is a "[MASK]". Is [NAME] "man" "woman"? [NAME] is "[MASK]".
These tasks are similar to the training task where gendered pronoun (she/he) needs be predicted based on given name. However, here we introduce gender nouns (womaniman) to test the model's ability to generalize beyond pronouns to other gender-related concepts. We test both orders of woman and man account for the effect of order.
We compute the mean male and female probabilities for the names from NAMEXACT depending on the split. Specifically, PP( P(woman) represents the average probability of predicting woman across all names (not just single-token names as for IP(F) and P(M)) of the considered split, and P(man) is defined analogously.
We present the results for both masked texts across all trained encoder-only GRADIEND models in Figure [6] The task could not be successfully adapted to generative models, interpretable probabilities could be produced as in the MLM setting. Hence, we limit our analysis to encoder-only models. For the different data splits, the results typically cluster closely together for the same model type. No specific pattern is observed, such as names from the training split being more biased than those from the test split. This suggests that GRADIEND generalizes well to unseen data, particularly names.
stable and
Based on the task, models above the dashed identity line are considered female-biased, while models below are biaee. Models near the identity line can be considered unbiased. For buse. BERT iarge and RoBERTa, the GRADIENDPemat and GRADIENDNale models are positioned as expected, above or below the identity, respectively. For RoBERTa, the opposite probability (i.e., ((M) for GRADIENDrematt) has a probability of less than 10%, indicating high certainty their bias. DistiBBERT behaves differently with low scores for the base model and GRADIENDFenatoMe and GRADIENDMale being misplaced in the female area.
BERT BuRT.
The GRADIENDFemmleMaal aims to debias the model and, in most cases, indeed lies close to the identity for BERT- based models. However, for RoBERTa, this debiasing effect works for one order but too strong for the other. For DistiIBERT, the GRADIEND is the identity, but probabilities are close to 0.0, making interpretation less meaningful. However, this is not due to debiasing but because the base model already has probabilities near 0.0.
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Interestingly, for BERT larges the base model is biased towards the last mentioned gender, but GRADIENDeemaleMaul consistently positions itself at the identity, regardless this starting bias.
This evaluation further suggests that, at least for BERT basc BERT large, and RoBERTa, GRADIEND generalizes the concept gender from she/he to other gender-related contexts, such as woman/man in this example.
J EXAMPLE PREDICTIONS
We present example predictions for the gender GRADIEND models in Tables 30 to 34 The top 10 predicted tokens, along with their probabilities, are shown for four sentences that involve profession-related gender-specific TPT:
The woman worked as [MASK]. The man worked as [MASK]. Alice worked as [MASK]. Bob worked as [MASK].
For decoder-only models, CLM task is derived by predicting the next token given the text up the [MASK].
All base models predict gender-specific professions based on predicted token. While there are some differences across the models, typical female-associated professions include nurse, waitress, and secretary, while lawyer, mechanic, and farmer more commonly associated with males. Some professions, such as teacher, appear to be linked to both genders. Decoder-only models sometimes generate non-profession tokens (e.g., professional, senior, and full) that likely precede a profession, reflecting their unrestricted next-token objective, whereas encoder-only models are constrained to single-token completion given the masked sentence context.
The GRADIEND models typically introduce new professions (not present in the base model's top 10 predictions) within their own top 10 list. However, for DistilBERT GRADIENDDomleMMtll there is almost no notable difference. In most cases, the newly predicted professions align with the model's expected bias. However, there are exceptions; for example, generates female-associale debiasing effect does not fully eliminate gendered predictions, GRADIEND FemaleMMlll demonstrates clear debiasing impact.
occasionally professions despite being intended to favor male bias. Overall, while the
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Table 14: Gender: Comparison of bootstrapped bias metries (SS and SEAT)) and language modeling metrics Streette GLUE, and SuperGLUE) for encoder-only models across different gender debiasing techniques. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold.
Model
88 (16) fa
SEAT
LMS (
GLUE (86) f
SuperGL.UE (%)
61. 61.2421.M8 .89
0.61 29
82.502:000 81
78.091
51.8221.67
DRADIENDremddoMM
0.78
00.49-1.08
4.0.10
0,51++0 0.10
$.044
82.09+0.55
110:28
78.37+ 1.65
(00.6))
52.38-1.88
08.95
0.799 24
0.33
82.2810.00 81
(0.000
78.4211
0..82
62.6521.88
GRADIENTAAI
1:38
59.86 1.94
10.18
0.43.
16
( 0.000
82.8910
10.18
78.2811
0..44
52.27
CDA
2.30
09.14=1.00 1,0
10.23
0.40+ 20
00.56
83.07 07..0..1
t0.90
78.90+1.00 65
11:33
03.16=1.80
DROPOUT
59.75=9.1111 .08
[0.11
0.45+00.22
25
20.75.0..9
[1:40
76.69+1 44
00:30
51.48:1.72
INLP
16.00
55.00++
10.24
0.377+0.19
1:10
83.68+0.50
10.00
15.11+1.65
( 0.00
51.02-1.55
RLACE
G1.51 .6
10.00
0.61
0:001:00220 29
(.000
82.4220.83
(0.10
77.991 59
60.95
LEAACE
00.10
61.1211
1.0.00
0.61
0.00
62.4810.88
10:10
78.00111
(.0.0
50.96
SECLYDEMASS
1.19
G0.05=1.04
0.002
82.47:00.53
SENNTEEBAS
1.00:
60.18 1.91
(6.27
0.34 13
0.000
82.49100.81 81
[L0.4)
77.6811 1
0.0.5
50.90
MeaneeeDeendeMMie INLP
6.377
03.077+ 1.77
10.31
0.30++0 12
(..81
83.31+0.79
(10.4
78.50+11
11.81
53.33--
GRADIENReendoMii SUNTDEMAS
( 11:59
69.65 .95
40.18
0.432 14
(0:14
82.062000 82
10.34
78.4311
0
0.0.0
CDA INLP
54.774
99
40.990
0.30 14
81.5510.08
1..00
78.1011
52.64
DRUPOUT SUNTDEMAS
3.07
08.17
10.25
0.361.0.14
50.64
11.31
76.7811.44
00044
51.42:1177
+ CDD SENTDERIAS
57.60 .97
0.38.1017
0.000
83.0020.00
(10.79
78.8811 55
53.24
DROPOUU INLP
5.666
84
0:27:10:12
0.033
15.15.0.63
[1:.0
76.53+
10:00
10.73:1.70
BF
61.26
0.521 36
82.8010
79.9611
53.74
GRADEENDRedd/AAe
5.66
65
16.00
0.554
11.35
81.5810.88
0..96
60,
0.446
54.20
GRADIENDRende
1.18
60.
10.15=1.80
0.008
1.10+0.11
0.092
82.06+0.77
1o..3
80.29++1.0
1.65
0.00
53.84=1.80
GRADIENDMLE
1.59
09.87=1.91
16.14 0.38++0.10 16
0.336
82.50+0.60
1..34
50.32+
00.30
03.84-1.87
CDA
2.00
59.26=1.00
16.11
0.63 24
0.68
03.57
11.30
78. 78.6341.44
0..28
51.02=1.61
DROPOUT
2.44
68.8221.8 .4
1o.17
0.691 22
R.
80. 9...8
(..088
79.4311 46
0.22
53.2221.00
INLP
59.33 .03
0.29 15
0.59
03.4120.70
1..30
80.28.111
89
11.00
12.14
RLACE
0.17
61.09=1.58
16.00
0.52+00.2 26
0.064
82.93+0.80
10.20
79.78+1
11669
52.05=1.62
LEACE
0.36
G1.00=1..8 89
0.53+ 26
82.94:00.00 0.00
(..38
50.26 24
035.841..67
SELFDERIAS
1:38
60.02=1.01
82.58+0.61
SENTDEBIAS
1.48
59.85=1.91
0:29
0.23 0.14
82.80+0.81
(10.Y8
80.
10..7++1.44 1.49
53.17+1.60
ORADIENPPendOAAA + INLP
1..78
55. 1.97
46.21
0.31
19019
03.07
f10.21
80.
19 25
51.30=1.00
GRADEEDRRaddMMee SENTDEBIAS
5.89
55.97+1..0
46.06
0.48+ 13
1:17
81.72+0.63
f.000
80.00+1
10.64
54.38- 1.87
CDA INLP
4..005
56.
46.16
0.38 16
1.40
84, 04..285075
11.66
78.3411
10013
11.87
DROPOUT SENTDEBIAS
57.
.04
410.05
0.48++0.12
15
80.86+0.22
79.45+1.15
53.36=1.74
CDA SENIDESIAS
02009
09.17=1.94
(10.08
0.00++0.22 23
0.79
83.60+0.79
40.91
7.
(1
54.00=1.80
DROPOUT INLP
56. 56.5811 95
4 0.6220.15 15
81.7220.81
(19.00
76.89 38
53. 1.733
DisiiBER
09.24=1.95
0.80 24
82.0620.60
74.47:11.00 1.99
49.69=1.65
GRADMENDRenntMMi
15..8411.00
0.80 24
82.01+0.80
1..00
74.45+1..0 60
10.90=1.67
GRADIEXDFande
3.30
56.05-1.90
46.01
0.80+0.02 22
0.96
81.08+0.61
(0.18
1..35+1.61
(6.63
50.32-1.63
GRADIENDAMR
61. 01.82=1.00 .00
(6.27
1.07 25
81.79.0.63
(L0.01
74.45+1.50 64
49.64
CDA
67. 27.29 .3
0.7410.22 21
82.2010.00
74.64. 46
50.7521.76
DROPOUT
0.17
46.02
0.761 26
1.999
80.8/10.09
110.00
75.1711.5
(0..8
50.27
INLP
4.00
55. +: 08
46.18
0.62+ 0.18
0.09
01.55770799
f.0.0
74.49+1.00
69
0..33
49.90=1.56
RLACE
1.39 57.85-
1.09
16.20
0.60++0. 14
0.07
81.99+0.61
(...0
74.51+1.69
(.0.03
49. 19.72-1.60
LEACE
4.435
54.91 6..01
0. 41.25
1:445
80.6220.61
[L0:34
74.22+1.
( (.000
49. 18.66
SELFDEBIAS
60.13-1.02
81.87+0.82
SENTDEBIAS
C2:39
06.922
[6.22 0.68
0.00
82.0110.80
(..08
74.541
0.000
49.75.1166
GRADIENDFendoMMee INLP
$6.17
54.07+5.00 .03
46.18
0.6 0.62+0.18
0..6
81.46+0.79
10.03
74.44+1.69
00.30
49.85=1.07
GRADEENTeedtoMee SENTDERIAS
56. 16 01
0.58 12
(0.33
81.9310.88
74.2611 60
49.94 66
CDA INLP
(3:43
55.83=2.00
40.23
0.57
0.43
62.40=0.80
1::95
11
51.09=1.72
DROPOUT SENTDEBIAS
0.16 59.08-1.98
40.38
0.50+0..1
1.
80.18+0.20 0.es
100.00
75.27+ 1.66
0.070
50.45-1.70
CDA SENIDESIAS
56.50
[]0.18
0.63:1.114
0.77
82.2310.0 81
(.0.30
74.791
(10.53
50.5221.77
DROPOUT INLP
54.90+8.91 4.33
( (6..38
0.48+0.11
(.020
81.82+0.62
(10.96
75.42+1.
11180
51.19-1.72
RoBERT
06.8021.88 888
0.
89.0022.64 64
81.651 1.44
53.3121.45
GRADIENDRendoMMle
2.91
63.89-1.00
46.30
0.45+0.18
JOFE
88.82+0.60
1.0.2
$2.47+1.05
55.34+1.47
GRADIEND
1..0
68. 66..644..00
6:38
0.56 10
86. 511.011
11.04
80.61 11
52. 52.82-1.60
GRADIENDA
8.64
66, 1.855
40.17
0.41
18
88.95+0.c0
60.26+1.0
53.79=1.47
.DDA
2.85
63.94 .2
40.18
0.4510.14
14
0..02
89. 19.1111.000 65
1:1.10
62.8111.41
0 89
56.20
DROPOUT
60. 30.334:.000 .9
0.49:
12
67.4941.29
1.77
51.05=1.62
INLP
1..00
62.71+1.0
16.14
0.44+0.14
89.08+0.63
1.66
83.27 1.61
1.77
50.06-1.60
RLACE
66.38 .8
46.00
0.58 17
89, 10.1550..03
60.59 53
(10.67
53, 13.0
LEACE
3.59
64.21=1.91
(1.000
0.58+00.11
95
87.04+0.09
100000
61.61+1.28
(h0 16
53.47=1.85
SECLADERIAS
1.79
1..0001..0
00.47 08.62220.03
SENNTEEIAS
64.88 89
[
08
0.49 14
0.00
29.1411..6
77.1811 18111.8
54. 54.5321.51
GRADEENTSonteeee INI.
6.6.5
60.51 .85
J0.25
0.33
13
0.30
89. 1010 63
2.200
79.6311
1.86
55. 17.1771160
GRADIENDfendoSSic SENTDERIAS
68.55
40.16
0.44
0.55
86.3720.00
(...9
75
26.2641 16611:0
1.44
51. 51.72-1.44
CDA INLP
62.13+
1.99
0.49+0..18
89.36+0.64
83
38. +1.+1.65
58.89+1.69
DRUPOUT SENTDERAS
37.38
16.06
0.642..12
9.88
80.26t0.00
4.4.6
26.89
03:29
+ CDA SENTDESIAS
61, 1.9
40.18
0.45+0..14
00:14
88,95=0.65
11.00
62 62.6441..8 82
1.899
56.28 28.
DROPOUT INLP
17.03
59.08
46.12
0.45++..11
3.566
85.4540.74
78.80++1.44
48.21
39

---

Published as a conference paper at ICLR 2026
Table 15: Gender: Comparison of bootstrapped bias metrics (SS and SEAT)) and language modeling metrics Stenntt GLUE, and SuperGI.UE) for decoder-only models across different gender debiasing techniques. Statistically significant improvements are indicated italics, while the best score for each base model is highlighted in bold.
(96) 7
LMS (%6)
(%)
SuperGLUE (16)
GPT-2
62.631 1.93
0.24:20.29
91.0220.62
71.73:1.00
45.4911:222
GRADIENDEendlMMi
59 2.009
0.33+0.39
00.44400.0
71.12=1.08
(10.00)
48.34+11.27
13.27 2.01
1..03
0..20
822.601
0.042
71.30=1.12
45.97:11.2
GRABIENDaak
65.061 1.84
0.000
0.33.0.36
10.09
91.1120.61
71.31=1.00
(10.00
46.0911:22
CDA
61.53+ 1.96
0.31 +020
90.65 60.655+0.6
73.20-1-2 1.22
48.76+11.38
10.09
0:300
0.000
00002
(0.16
INLP
12.21) 60.36++ 1.95
0.23+0.26
91.25-+0.8
71.75-1.18
(:0.00
45.78+1122
KLACE
( 9.08 58.04
L0,02
0.22::2244
75.0610.99
0.059
72.31=108
(o.44
45.9241..2
F.FCC
(11.40 61.2311.0
( 0,000
0.24110.2
0.007
90.9620.61
20:14
71. 71.5821.00
(10.35)
45.8311
SELFDEBIAS
61.80+1..0
88.04
SUNTDEBIAS
46.39 56.04
0:0:3
0.34
3.55
87.4520.71
00.20
71.4621.11
1.118
44.3311.15
GRADIENDeaulsMii INLP
$.652 57.96
f 0.00
0.31
0.27
90.75+0.61
0.88
71.20-1007
1.0.89
46.30+11.2
GRAnINNDenacMMa SunIDmmu,
69.9412.00 2.00
0.318
0.4220.25
90.24:20.2
71 11.5221.12
45.961 1:24
13.78
58.90118.0111000
0.00
(1081
91.8810.58
11:38
73. 11.224
1.139
46.8711.33
DROPUCT SENIDESIAS
4..00
36.077:2 2.000
0.18
0.42200.19
6.99
85.04
(70.88
72.27=1.25
1:1:28
46.761
SENTDERIAS
55.56
66.11
0.36
0.40.0.22
11.23
89.8110.63
1.33
73.0821.27
(.0778
46.2711
DROPOUT INLP
$3.2
59.421
0.222
0.4610.23
0.00
90.99.1.60
0.02
71. 71.11
46.591
69.4411.77
0.9310.16
02.4220.53
45.86
54.4612 2.6.2:255
GRADIENDEaausMMi
10.23 69.21 1.75
0
0.07#0.10
4 0.24
92. 18-108.0.00
1.002
46. 1.91
50.97 007++22200
GRAnIENDeenak
11.48 67.961
1..00
0.87.2.14
4 02.3320.50
63.11
GRADIENDMOL
10.00 69.51 1.76
40.11
0.82+0.11
10.16
42.39-2.00
2.00
52.30++200
INLP
22.83 66.6111..
0..23
0.70
4 0.44 91 25.0055
( 0.33
43 1.78
49.5V1
RLACE
69.7441.78
0.93++0.16
92.47=0.53
46.03=1-1
49.97
LEACE
(..03 69.47+1.73
0.00
0.92
0.05 92.47
0.033
46.
SELLDEBIAS
$.995 69.6911.80
19 61.2810.99
SENTDEBIAS
42.00
99.92
0.0.04
1.1.3
54.1242.37
GRADIENDremalMMa
10.40
0.225
0.0.0
91.53.10.00
11000
16.8
( 11.9
45.49.4
GRADIENDEanioMMi SENTDEBLA
0. 0.03
89, 09.93+0..4
46.17=1.00
50.40++2.16
LL-MA Insirucl
68.53
0.00
92.21
19.14=1.02
56.07+12.29
66.2411 1.87
0.4920.15
89.05 0.060
47.37 1.81
59.00-1...0
GRADIENDevnde
12.10 68.3711
0.0.1
0.71
00.39
3.02
(12
GRADIENDMOk
1.001 66.92+ 1.58
10.39
0.71
90
00.37
60.47+11.80
1..78
56.36+2..22
INI.P
(13.40 66.13.1..0
0..33
0.57
01.99100 20.55
48. 19 1.55
(.0.72
67.35-1
RLACE
(10.3 08.70:11.80
0.0.0
0.70:20.20
0.000
(.0.10)
49.24=1.00
0.000
58.0242.22
I.FACE
[0.37 68.1611.88
(10.20
0.60.0.20
0.05
92.26 20.55
(.00)
13.1
0..23
67.8412.
SELFDERIAS
1
59. 1.00
SENTDEBIAS
11.79 66. 66.74+1.54
00:47
0.43
1..00
92.24=0.55
0.06
1..48
58.56+
CRAAnINNDemaaMa INLP
4.886 69.651 1.87
( 0.660
0.39 20.
8.809
( 3:39 46.78
(
50.000000
GRAmunDeemiitii SuNIDumIx, 66.124 1.89
14
89.91 62
48.23
40

---

Published as a conference paper at ICLR 2026
Table 16: Race: Comparison of bootstrapped bias metries (SS and SEAT)) and language modeling metrics Streester GLUE, and SuperGLUE) for all models across different race debiasing tech- niques. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold.
Model
88 t.0
SEAT L
I.S (96)
(S)
SuperGLUR (9)
BERT
57.04
0.521
82.50.10.88
78.
51.62:1.00
GRADIENDA
41.55
56.1521.00
11.00
0.601:0
00.23
82, 20.80
T0:44
78.66
19.50
..89
56. 26.92211 .1
0.601: 24
81 .3.2022
78. 78.74411 1.61
32.3611..9
GRADiENDmaaani
0..25
57.27=1.00
40.01
0.514
82.37=0.50
10.28
78.37
(10.5
52.65+1.88
11.00 11:05
58.08 58.0621.00
0.36
0.26110.3
( 0.065 81.8520.82
10:39
77.881
11.15
6..98.1..2
DROPOUT
4.0.84
56, 06.50++102
10.07
0.44+0..17
80. +..89
11:40
78.09++1.4
51.48+1.777
INLP
40.00
56.9 06.97
4..00
0.601 19
10.38
20.00
40.24
77.861
10.44:11.54
STOLEDERIAS
4 2:39
54-48=7.06
(1 050.09
82.56 0...6.3..3
SENTDEBIAS
56, 26.655:1.00
0.52#0.26 26
82.48 0.10
1.0.00
78. 78.14 68
50.67=11.44
57.00
0.454 10
82.89.10.80
79.98.1 31
53. 33.74411.60
GRADIEND Autiitisk
19
00.04
0.49...13
82. 02.4411..81
80.38.4 53
1.0.55
GRADIEND GRADIEND Aihe
33.6
54.00=1.01
0.52+0013 10.37
31.33
81.77.0.53
10.90
80.88+1..0
1..55 54.58 1..86
GRADIEDDRLLaWii
56. 96 .02
0.47.
82.66 0..0.1
60.4911 54
1..42.1.877
CDA
57. 00.000::.000 .03
0.41.t0.13 1:013
82. 0..00
77.97 97
53.15:11.77
DROPOUT
(.0.91
56.092144
10.05
0.4210.12
38..8
10.55
79.43.1.00
( (.0.52
1..22.1..6
INLP
10.03 57.01 .04
1..00
0.45::0.11
0.117
83 0.7077
10.08
80. 00.02211.20 29
0.0.24
53.50=11.0
SELFDEBIAS
41.02 35.98+1.02
0,000
82.88+0.79
SENTDERIAS
16.12200 (...1
1..00
0.45 10
@ 02
82.87
1.018
80.10:4: 5a
000007)
33.68
DistiBERT
36.09-1.04
0.30+0.16
82.06 0.000
74.47+1.69
49.09+1.66
GRADINDDAusells
1.58 57.44
(.0.0
0.32 16
1 10022
31.79.10.80
1.0.00
74.41.111 60
(10.00
49.449..8 10.69.11600
41.01
56.0821.00
(10.39
0.301 16
0.063
28.0.1
40.12
74.34411 80
0..38 1.707
GRADIENTmissWhie
(.0.08
56.0121.00
(0.03
0.331
0.225
31.7810.81
4.0.00
74.38.1 47
(1:440 49.29. 19.70
CDA
(10.87
56.95
(0.00
0.36+
0.773
81.36 2..63
10:18
74.00-t:1.0 4.
11:13 10.80#11.79
DROPOUT
57.21
(
0.41 1.110000000
80.24 2..05
(.0.00
75.1711.11111 50
(7 58) 3..27:1.775
INLP
4.0.54
50.54:1105
[ 0.009
0.21+0.15
40.39
10.18
74.64+11.67
1:144
60.83#11.0
SELLIDEBIAS
54.8021.02
81. 81.9120..1
SENTDEBIAS
4.0005
36.06.21.00
(10.00
0.30 16
81.6620.51 00.40
10.10
74.57
40.47.11.62
RoBERTa
60.13.20.97
0.4311.17
80.0920.61
81.6511.00
33.31.1144
GRADIEND Aiae
4 6.29
59.88-1.00
( 02 0.44+0.12
85.26 06..78
15:36
70.07 +..00
46.
64.56 99
0.40:20.14
85.71 0.
(5:50
79.
86
50.00 20.04 1.44
03.30
65.50_60
0.4210.17
7 0:40
$5.00
76-45111
(70.4
53.72.11..
10.49
0..997
( 0.005 0.
85.
40.07
81.08+
1 1:447
07.48+11.71
DROPOUT
$4
58, 38.09=0000 68
0.61+0.16
85. 0.770
67.49+11.4
51.05=11.22
INLP
40.63 59.31-0.98
(10.02 0.45+00.16
88. 07.66
40.11
51.54+1-48
(10.46
53.77+11.2
SULLIDEBAAS
57.82=7.00
0.
88, 79.669
SPINTDERIAS
60.42220.97 (10.28
0.4410. 17
80.0120.64
(3.12
78-4841 -00
36.54111.48
08.90220.9
0.47.10.33
01.0220.62
71.734 08
45.49:11.29
GRADIEND GRADIEND Bokk
5.887
53.03-1.01
46.97
0.40+0.30
0.
90.75-0060
10.58
71.14+1.01
(10.04
45.15+1..18
40.45
18.00
410.06
0.41 27
@0.00
90.98.0..0
1.008
70.65::0.06 88
(10.43
45, 45.02.1.22
GRADIEND
59.01=0.09
(10.00
0.48+0033
91.01=0.02
[0:39
71.50+11.07
45.97+11.1
CDA
40.42
58.48
11000
0.4910 29
2.
85. 05.15
1.74
73.4741
(11.47 46.96 11.27
DROPOUT
41.45
37.42.1.00
00.05
0.3940.38
$9.59
90.33
10.02
71.70#1.15
(10.46
4.444
INLP
59.00
59.00-0.08
0.47+0.35
71.45++1.08
40.745.7774 60.77++1.22
SELFDERIAS
4.:45
55.45=1.0r
0.
SENTDEBLAS
48.40)
56..4-1.01
0.010
0.37+0.21
( 0.38
91.38 +0.59
10.18
71.55+11000
44.73+1.26
LLaMA
65.06=0.08
0.21#0.08 08
92. 02.42-0.000
45. 86.8 98
54.46::2.28
68.86=7.000 1 08
0.23310.00
89. 1.066
49 49.44411 97
52.02.2..77
40.99
64.07
(10.08
0.241: 11
0.776
91.67.0.00
1:120
07.06.11.0
0.005
54.40....2
GRADIENDgi.aWhn
10.63
41=0000
(0.03
0.22++008
0.442
92.01 00000
41.40
44.40+2.00
0.0.0
53.76+2.03
INL.P
( 28
66. 29.
0.000
0.21.10.00
( 02, 2..0
11.08
47.79.1. 87
54.54.9.12
SULrDEBIAS
4.79
29.28=2.04
2. 88)
0..24t10.09
SSINTDERIAS
(0.00
06.0220..9
0..03
0.221.000
( 030003
92.39.20.51
1..82
46.38-11 9.3
0..39
64.85.12..0
LLaMA-lastuce
G3. 03.72
0.34
92.21
49.14411.92
58.07
GRADIENDAsietiie
3..19
60.55=0. 55=00.00 08
0.86 066+00.0
4.49 44.81 1.004
37 4:
22.15+2.15
BRADIEND
4 9.50
54.22-1.000
(10.05
0.39+0.05
01.07.t0.000
12:38
36.
76
4 15
2.47
4.0 68,
03.03229.9
(10.13
0.4510.13
( 1..08
92.
20.26-200 26.0.
48.
48.06-12.00 022
(1 0..88
68.66 22.00
INLP
43..23 63. 1.00
(0.03
0.35++0.14
0..00 92.21+0.54
F0.77 49.
1..91++1.8
010.38 $5.35++2.34
SELFDEBIAS
$3.93 57.81 27.81==1 11==1.00 60
19.1..066
SENTDERIAS
4 28. 63. 03.43
( 0.000 0.34+0.14
@ (.
92.00=00.56
10:10 48.9 1.99
( 1700.4 58.53#2.41
41

---

Published as a conference paper at ICLR 2026
Table 17: Religion: Comparison of bootstrapped bias metries (SS and SEAT)) and language modeling
metrics (LMSSttereSees. GLUE, and SuperGLUE) for all models across different religion debiasing techniques. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold.
Modd
(5) 7
SEAT
L.S (96)
GLUU (96)
SuperGL.UE (%)
BERT
52.7743
0.38
02.5010.81
78.09
51.8211
13228
66.06443.00
(70004
0..22.0.21
(10.06
82, 64..811..1
(10.24
38.33:11.88
63.00:11.8
Gie GRAADENN PranvinsShaddi
54.03.13 68
0.47.10.101 18
82, 02.51:4 51..551
78.4411111 1:557
62.54-1 1.88
10.91
51.86443.0
C1:31
82.39
11.25
76.374
1..89
52.71
CDA
9.440
55.21
[L0.20
0.1611.00
00.32
82.8220.81
00.27
78.36.1149
1 1:36
58.
1.8.8818.83
DROPOUT
12.11
50.07 45
0.000
0.38++0.1
80. 80.75+0068
76.09+1.44
(1.88
61.48++..75 +-.++1.72
INLP
10.04
62.223::3.
.,000
0.36.10.14 14
(00667
81.83:1.822
40.39
77.7113.22
1.33
50.701
STI FDERIAS
118
51.45435.0
( 05
82.65.1..82
SENTDEBIAS
49.07 62
0.36:40.21
0..35
77.68
(0.07
51.25
BERT bags
56.1243.
0.7540.28 24
82. 02.80.00800
79.98
53.74
54.16433.
0.86.10.2 23
82, 03.
(1..00
80. 8..88811.00
(10.88
54.52
11.76
54.36++3.66
0..04
0.79++.20
40.35
02.581
10.35
80.3 10.36+11.6
(..883
54.58+1.580
GRADIENDD
2:55
38.66-13.55 51
0.73.0. 14
10.25
83. 14 78
10.54 80.6211..0
54. 18:11.85
CDA
64.2443355
0.60:t0.18 18
82, 0.000
80.4 80.42 1.
64.27: 1.
11.64 54.4815.M
0.36
0.9110.26
2.55
00.55
79.43.1.40
(..52)
63.2211..0 12221.66
INLP
11.92 54. 34.20.33.47 47
1.0.19
0.58:10.17
4.0228
82.61
4.013
79.66:11.06
(L0.88
53 13.21:1.00
SELFDEBIAS
1..10 52.96++3.68
10:33
7400.0
SENTDERIAS
55.85.13.55 40.37
0.33
0.634...2
82.75.0.00 (
1
80, 60..8841..4
(Lo.07)
$3.87 1.7.11.60 64
DistiBERT
55.40+3.71
0.32++0.2
82,06+0.50
74.47+11.5
99.69++1.65
GRAADIENDssainntonnn
11:20 54.20.3..1 73
0.002
0.34 27
(.0000 00007
92.00.20.00 81
(10.02
74.49.11.00
(10.00
49.75.1 69
GRADIIN MaaisiasMaaiiinn
11.18 54.2213371
1..05
0.37.19.27
10017
81.8910.80
74.6011 10.03
f...7
49.761
GR PRRAAIEEDDaiieMMuo
11.97 53.4243.77
0:32
0.4410.22
(30.43
81.66.0.881
40.07
74.40.1..4
49.91 11..1.00 (
CDA
1o.66 56.
[o.11
0.22
Q 31.78800.81
10.49
74. 184
10.00
50. 19
DEOPOUT
10.07 56.0613.55
0.25.10.1..
80.24
(.0.70
75.17.1..0
(10.58)
10.27:1.75
INLP
10:30
33.75433.11
@ 0.000 0.26-+0.19
4 48 81.00-00.81
(10.18
14.59+11.57
(10008
49.64
SELDDEBISS
52.29::3.00 80
81.69:10.63
SENTDERIAS
(18.11
52.2813.70
20.05 0.2010.21
4.0 27 81.8020.81
(10.00)
74.5411.60
(1012
40.81
RoBERTa
64.6613.33
0.3911.21
89.0911.61
81.6511.44
53.31
44.0% 60.59+3.33
0.00
0.33++0.14
88.14+0.08
9.000 72.07+1.00
1.0000
52, 02.66++1. 0..66++..00
GRADEEN PCamitiaDSaaii
54.88 39
0.39.10.0 18
88.65.20.69 40
88.06 1.555
56
00.66.11 .4
14.88 50.8315.15
0.25
88.9010.40
11.00
82.7111
22210
51.1211.0
CDA
1c.07 58.6943.65
L0.21
0.18
3..39
1 10.53
82.4844..55
4.77) 58
DROPOUT
16.01 58. 05
(0.00
0 0.38-20.18 18
33
85 85.34 72
67.49=1147
51.05 105+:1..0
INLP
11.70 62.96++3.98
(0.02
0.38++0.21
00
77.70+2.61
54.95++1.6
SELDDIBISS
12.71
61.95113.29
0.330
74.064
SENTDERIAS
61.4913..00 13.17
0.46.0.22
80.05
80.61
52.71
63.22133.50
0.3611.27
91.0210.62
71.73:11008
45.4911 1.28
GRADIEN Daniiiateat
1o:21
0.00
0.36++0.2
00.18
90.87+0.03
10.00
71. 71.73++0.9
1.11
9..0
53. 9:3.51
0:3
0.49.0. 26
1
89. 96
41.56
16.11411.4
1.54
47.0211.38
GRADIEND GRADIEND
12.16
61.06-+-3.1
0.48++0.21
89.84+0.05
(10.05
71.78+1..
46.64+1..0 1.221.2
CDA
13.87 67.1013.
m.008
0.40.1.33
49.441:
(1..50
73.3241 12222.1.2
2.00
48. 101101114
DROPOUT
11.78 64. 94.66633.64
10.05
0, 20
40.69
30.33=0.62
40.00
71.70.1.11
(10.48
15.94#1.44
INLP
63.91+3..5
0.35++0.27
91.19+0.00
71.52+11.6
45.77+1.22
SELFDEBIAS
[4.01
88.89
SENTDEBIAS
43.00
59.62++3.64
(0000 0. 0.43++0.28
90.53+0..4 4
(10.16
71.68++1.6
(10
46.29+11.2 1.22
1LAMA
66.441:3338
0.28:20.00
02.02.0.
15.66:11.98
54.46: 28
GRADIIN Pehnsiasdewwa
62. 117.13..1 41
(0.03 0. 00266.1.11 11
91.21 01.21.2.000 58
7..4
98. 28.982...00 06
52, 32.56-22.11
1..00
0.21
4.0 18
92.27
1.44
44.46....0
2.88
52. 52.11
GRADIEN
1..7
57.74 4.3.5
0.008
0.36
42.50
90.28=0.62
10.69
16.54.1..0
1.88
52.
32.59:22.17
INI.P
11.74 64.71 1.3. 38
0.00 0. 0.26.10.08
(7.
92.42220.50
(12.28
48. 1....4.... 14:.1...
f.0.21 70.21
54.666.66
64..66.2..0 30
SELPDEBIAS
11.41 65.03:13.95
61.2851.00
SENTDERIAS
42.65
63.804.5.0
( 0.003 0.26:10.08
1 10.02
92.44420.53
(10.04
45.6241..10 1.2.1.90
(0.17
54.29.1
85.83-33.35
0.20:20.09
92.21:20.34 20.54
49.14::1.92
08.07
GRADIEN GRADDEENDesnviisdeevan
10:30 66, 3.
0. 0.21+0.00
12.12 92.
32.3440006
(1033
49.45-+2.00
1 12000 60. 1.:11.05
GRADIEN DearitiinMaaaiii
12.98
47.00-+3.02
0.660
0. 0.89+00.15
16
75.
44:44
44.0#11..0
(.300)
23.77
GRA GRADIIND CRAADINNDeiieete DjxxieMMullin
(1.00
63.92413.05
(.0.0)
0.60:20.11
4
90
10.00
49.23411.95
( 29.7422.18
INLP
11.40 64. 43-+3333
(0.03
0.19++0.00
91.81+0.22
1.058
49.64+1.00
(0.18 57. 2.440
SELFDEBIAS
44.16
61.68443.0
98
59. 18.
SENTDEBIAS
12:88 62, 92.44433.42
(1 [10.04 0.16++0.08
@ 14
92, 02.088+0.56
4 48.799+1.97
Th 58.53++2.41
42

---

Published as a conference paper at ICLR 2026
Table 18: Gender: GLUE bootstrapped validation set scores with sub-results for encoder-only models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold.
Model
CoLA
MALG-M
SINLAAM
MRPC
QNLI
QQP
RTE
SST-2
STS-B
WNLI
Average 1
BERT
55.60
83.40
83.97
86.32
90:19
90.21
90.99
91.34
88.71 71
65.83
78.09 1.00
53.04
83.63
84.25 29
96.99
90. 66
90.40
633 86
91.57
58.16
66.80
78.37 1.
GRADIENDiemak
53.54
83.
86. 66
90.35
57.22
110 88
78.42+11.60
GRADIENDAGL
02.24
83.51
84.21
86.94
90.65
90.34
633 545
91.56
88.19
06.77
<t
7H.
1.68
+ CDA
54.73
83.
84.14
90.
90.56 50.56
90.
65. 76
91.22
66.77
56.30
10.90 78. 1.
DROPOUT
46.09
82.04
83.36
97.85
90.51
89.77
61.47
91.71
$4.84
55.00
1.1.40
T6.69+1144
INLP
03.97
83.
84.12
87.09
90.07
90.23
61.48
92.10
58.32
05.30
( 02:
78.11
+ RLACE
55.47
83.
83.90 90
86.
90.
90.20
60.70
91.16
58.69
55.83
(10110
17.99++1.00
LEACE
00.28
86..20
06.36
10010
TH.UD:t: 1.
SENTDEBAS
64.50
83.57
83.92
87.01
90 25
90.22
61.77
91.57
88.46
61.60
(10:40
77.68:226 1...2..00
+ CRLANENDRGUbMMAA + INLP
53.91
83.52
83.88 84
87.25
90. 558
90.39
64.92
91.37
57.88
56.35
T8. 78.50++1.4 4.442
GHADUNDDeuubttte SUNIDE
83.60
84.22
86.96
64.21
88.36
66.80
(T 05)
7..43:11..0
CDA + INLP
54.34
83.09
84.11
89.73
90.64
90.34
64.68
91.75
86.55
51.74
(1
78.19+++.. 1.44
DROPOUT SENIDEMIAS
46.09
82.83
83.68
87.95
90.64
89.80
61.96
91.71
$4.80
65.00
1.1.88 88
76.788:::1 7.8.778::444
CDA SENTDEMIAS
54.47
83.80
84.05
90.45
90.55
90.21
65.76
91.56
66.74
66.30
78.88
1.55
DROPOUT + INLP
46.04
82.07
83.62
87.67
90.33 36
89.
62.
91.83
83.98
54.07
76.53 1.40
BERT bags
62.19
86.19
86.38
88. 62
9/2
90.50
66.69
.1
68.62
61.50
79.98 1.31
60.14
85.58
86.08
80. 93
92.16
90. 58
66.10
92.84
60.20
65.36
1
GRADIENDRont
61.53
85,85
86.07
87.76
9...98
90.25
66.51
93.10
69.23
56.27
80.
1.
CIR CREAHENDMME
62.25
85.68
86.20
88.08
92. 06
90.63
65.61
92.76
89. 50
66.27
80. 60.32:4 1.55
+ CDA
61.38
80.06
196
89.
912 U4
90.56
69.44
9:3.00
88 .6
46,90
TH. 10. 7M..63- 4.
54.54
85.95
86.11
65.85
88.24
64.86
4
INLP
60.00
85.78
86. 27
89.56
92. 11
90.20
67.55 57
02.72
69.46
64:74
(
80, 28.28.12 1: 1.30
RLACE
86.29
38
21
52.98
1 20
79.
FACCE
62.70
85.85
17.
88.
91.69
90.34
68. 07
92.83
69.03
62.50
(
80.
22..1:1111 1.
SENTDEBIAS
62.65
86.06
86.47
89. 85
92. 08
90.47
67.43
93.26
69.27
55.31
10.76
80. 73:77++1.400
PeNNINNeebbbM INLP
61.18
85.66
86.21
80.62
91.89
90.47
65.85
92.96
89.45
64.34
(10.91
80.11::1 6..19:111.2
GRADIENDemaAMi SENTDEBIAS
59.87
85.60
86.13
89.78
92.02
90.50
66.35
02.95
69.22
63.47
0.000
80.
1..00.11.000
CDA INLP
61.26
86.48
86.98
80.87
91. 90
90.60
69.76
92.88
88.49
44.70
78. 14..34::1.. 1.
SENTDERIAS
3.14
85.62
85.75
73
+ CDA SENTIEBIAS
62.90
85,50
86.05 06.05
90.03
9.
90.52
62, 62.80
92.78
68. 60
46.45
(1..91
79.07 1.88
DROPOUT INLP
87.35
85.58
86.
90.21
92.19
90.38
63.96
91.87
46.35
4:3.09
76.
DiSEIBERT
43.90
80.57
81. 24
85.79
.9
13
61.
74.47+11.60
ClRA Arineeeeeaatt
80.60
55.02
90.70
(1. 02) 74.45.1
GRADIENDReut
43.36
80.58
81.22
85.76
87, 26
88.99
54.56
90 00:47
81.58
56 27
11.312
74. 1. 1.661
43.91
80.80
81.26
85.80
27.00
80.02
55.73
80.61
52.00
64.96
40.003 01:
1..45:1.55
CDA
43.73
80.67
81.42
86.84
58.
90.35
02.64
(ho 74. 6.44
DROPOUT
43.16
80.30
81.14
87.91
87.41
88.85
60
90.37
$2.99
64.50
(10.70
75.177 1.50
43.63
80.63
81.10
85.
87.16
07
55.93
90.80
81.50
56
74.49.11.00
RLACE
44.03
80.57
81.19
85.68
68.66888
86.90 96.99
89.03
55.26
90.78
61.71
56.27
(10.06
74.51
L.FCCE
42.92
80.66
81.18
85.64
87.08 .0
80.02
64.44
90.62
81.65
65.82
74.22.1 1.54
SENTDEBIAS
44.14
80.73
81.17
(10.08 74.54#1.50
DeeaeNNDeeaeMMta INLPP
81.13
87.19
65.96
81.60
56
(1. 03: 74.4411..0
CRA GRADENNDDeAAAM + SENTDERIAS
43.55
80.57
81.22
84.95
86.90
89.03
54.81
90
81.74
56 27
(10:21 74, 22..2611.. 1..11..00
CDA INLP
44.22
80.61
81.43
87.44
87.15
58.91
58.90
90.55
52.94
61.29
(10.20
74.711111 1.33
DROPOUT + SENTDERIAS
43.49
80.37
81.08
88.
87.42
88.80
60.12
90.37
88.00 04
65.41
(
75.27 1.55
+ CDA SENTDEBIAS
43.80
80.60
81.43
86.84
87.40
88 88.98
57.82
90.34
62.91
54.05
74.79+1..4
DROPOUT INLP
42.34
80.32
80.97
87.23
87.83
88.81
63.31
90.06
62.68
55.91
75. 4.44 1.0.6
RoBERTa
62.67
90.08
91.00
94.12 12
90.95
68.17
94.88
91.04
52.03
81.65:1144
Gle DienDDDDeeeeeet
60.27
89.86
89.60
80.
94..4
91.004
75.88
95.57
91.82
63.89
( 92 $2.47.1
GRADIENDivaas
61.45
89.95
89. 99
94.18
91.00
72,
80 80.99
54.83
1.65
GRADBINDMAR
61.52
( 37
1.50
CDA
62.95
90.18
89.62
91.43
24
91.000
76.
95.94
82.82
51.15
1 1.0
11+11.41
DROPOUT
24.12 58.46
53 .99
89.79
94.45
90.53
61 LG
50.98
88 778
64.36
07
49.2 1.47
INI.P
02.65
94.41
91.47
56.27
1 02
8..27
RLACE
61.23
78
71 61
90.01
94.34
91..99
75. 81
95.72
91.44
53.87
(
60.59
L.EACE
02.87
80.50
89.55
80.44
94.31
91.58
72.44
95.06
91.64
47.74
410.00 03
11.6411..22
SENTDERIAS
28.58
89.85
89.64
91.27
94. 29
91.17
68. 65
95.80
91.40
49.71
4:2:29)
77.
78.18 18
GAAIENDEEEAAA 4 INLP
64.94
71.88
72 61
89.6T
79. 71
91.43
76.61
95.42
9X..1
66 27
1 79
73.63321..8
DRRATENDDeembMMe.. + SINTDERIAS
61.26
41-48
41-40
89.68 69
79
91.34
76. 86
95.54
91.98
50.19
75.86
CIAA INLP
61.27
87.
94.44
91.83
56.27
11.78
83.
+ DROPOUT SENIDOMAA
96.29
89.60
80.77
85.84
94.11
90.92
60.60 08
95.82
88 .8.
61 .46
76
1.
SENTDEBIAS
79. .9
95.77
49.23
+ DROPOUT + INLP
30.25
89.84
89.58
87.94
94.37
81, 78
$5. 99
80.69
87.65 67.65
55.79
47.68
73. 73.500+1..6
43

---

Published as a conference paper at ICLR 2026
Table 19: Gender: GLUE bootstrapped validation set scores with sub-results for decoder-only models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold. GPT-2 results were computed after fine-tuning and LLaMA-based results were computed with zero-shot evaluation.
Moded
ColAA
UNELAM
MRPC
QNLI
QQP
RTF
SST-2
STS-B
WNLI
Average
GPT2
20.51
81.10
82.02
83.75
87.64
88.59
59.87
91.45
80.30
51.96
71.7311.00
14.21
81.07
81.91
83.87
87
88. 58
61. 02.68
91.72
80.41
49.79
40.01
71.12+11.08
GRADIENDganlk
11.43
81.03
82:08
83. 02
87.60
56
63.
91.61
80.81
53.46
(L.42
71.3011.12
GRADIENDMa
16.22
81.08
81.89
87.
88.45
62
60.75
49.80
(1:409
71.31+1.09
CDA
32.79
80.82
81.90
84.
87.70
88.60
61.80
90.72
80.96
50.35
1:4448
73 20.1
DROPOUT
20.09
80.57
81.74
83. 68
87
62 62.68
81.07
50.48
71.70#1.15
INLP
81.99
87.
88.54
51.02
1.002
1.14
RLACE
23.21
81.06
82.05
83. 77
87 87
86. 89
62.51
91.38
80
52.02
72. 11111.00
20.05
81.99
61.19
60.53
10.18 71.58+11
SENIDUBIAS
18.97
80.98
81.97
83.63
87.02
88.58
61.70
91.37
81.26
48.75
00.36 71.4611.11
ORADIENDROAAMIS + INLP
13.85
81.09
81.68
84.34
87 37.33
88 56
63
91.88
60.47
49.79
1.088
71 1
CHAAHENDguubbMa SINTDERIAS
9.97
80.97
81.83
83.87
87.28
63.27
81.63
55.90
0..20
CDA INLP
38.91
80.88
81.00
84.
87.67
88 61
63
90.65
61,02
46.94
1.88
73. 11.1111111
DROPOUT SENTTEESAA
21.52
80.92
81.73
83.34
87.
87.94
83.53
90.
82.22
52.82
72.27 1.22
SENTDERIAS
11.31
73.03
DROPOUT + INLP
19.24
80.57
81.73
83.52
87.05
88.
62.80
91.03
61.06
51.46
(10.09)
71.70+11.1
LLAMA
-8.08
34.96
35.97
69.
49.
37.344
54.19
74.03
64.86
45.86:11.90
+ GRADIENDgauukMiie
-2.30
35.12
36 16
72. 83
55.56
39.18
52
69.60
58.95
48.88+1191
GRADIENDsuulk
54.88
(1 3.33
+ GRADIENDyOle
-6.67
34.67
35,
48.08
51.88
39.94
$4.56
58.06
57.70
(..47
42, 12.39-+2.00
INI.P
0.00
92.26
32 66
_1.10
40.45
36.55 83
48.04
61.66
66.27
(L0.18)
45.7311
1.0.7
LEACE
-9.46
34.27
35. 06
72.07
49.
37.007
53.75
75.39
56.27
fo.32
48.17++1.99
SENTDEBIAS
7.12
35.10
40.95
74.40
56.27
47.1811.0
GRR CRADIEND)pmaaaa
SUNTDERIAS
-2.17
34.83
35.12
36.11
36.46
79.51
51.20
89
37.26
53.05
522
68.85
62.60
67.59
1.02
(10.09
46.8511:.91
46.77.11.88
16.85
48.12
47.97
4.67
37.34
63.30
64.68
73.00
06.20
19.14411:.2
GRADIENDEaukMMik
2.64
25.76
25.89 89
79.
19.16
39.26
53.16
61.62
57.90
47.37+11.81
GRAABENDgaukk
45.80
45.91
54.87
87.1
66.39
(.3.08
16.12:1165
ClRATHNNDAaI
14.80
46.62
45.98
79. 76
70.
68.98
70.99
77.16
56.05
21.23
60-47111.0
INI.P
16.09
#4.95
44-47
0.68
57
63.09
66.00
73.54
67.58
(0.95)
48.19111.88
+ RLACE
17.03
48.19
48.20
4. 89
37
63.39
64.22
72.71
66.
(.010)
19.2441.93
LEACE
16.74
48.51
48 08.06
57.17
84.97
66.15
10.03
49.13+1.99
SENIDUBIAS
16.26
48.46
48 20
4.22
56.07
63.11
64.97
72.94
66.15
[0.00
49.05-t:..9
ORADIEND INLP
-0.94
35.86
36.18
81.01
47.15
36.89
54.52
58.99
60.70
46.78+1.85
ORADIENDSuubbMia SENTDERIAS
[0.93 48.2311.8
44

---

Published as a conference paper at ICLR 2026
Table 20: Race: GLUE bootstrapped validation set scores with sub-results for all models. Statisti- cally significant improvements are indicated in italies, while the best score for each base model is highlighted in bold. ILaMA-basee A-asedd results were computed with zero-shot evaluation while all other scores are derived after fine-tuning.
Model
CoLA
MRPC
QNLI
QQP
RTE
SST-2
STS-B
WNLI
Average
BERT
$5.60
83.40
83.97
86.32
90.19
90.21
60.95
91.34
88.71
55.83
78.09
+ GRADUNDAAnrfllt
51.07
83.71
84.07
87.83
90.24
90.30
66.87
91.37
88.
56.76
75.5621..0
83.62
69.03
(.0.05
78.7411
+ GRA
51.51
83.62
84.04
67.78
90:26
90.27
65.87
91.68
88.29
55.87
9.0.2
78.37
+ CDA
49.85
83.55
84.08
80.67
67
90.65
90.37
63.43
91.23
87.38
54.49
(.0.22)
77.8821.48
DROPOUT
46.09
82.64
83.36
87.85
90.51
89.77
61.47
91.71
84.84
1:40
76.89+11 1.44
INLP
54.36
83.67
84.12
86.66
90.64
90.30
60.43
922 21
88.27
53.97
0.0.24
7T.8G
SUNTDRREAS
56.00
83.50
83.95
86.20
00:21
90.26
60.69
92.07
88.71
55.36
78. 14:11
BERT hape
62.19
86.19
.88
88.62
92.22
90.50
66.59
93.31
88.52
51.69
79.981:
60.70
85.50
86.00
88.57
92.21
90.60
66.80
93.
89.42
66.27
0.400
80.38111
ORADIENDAGHWWi
63.00
85.56
86.06
90 30
92.19
90.62
67:28
92.92
89.47
56.
0.99
80. 80..88... 88 1.03
+
CRADIENDEua.Whie
81.61
85.64
86.21
88.96
92.04
90.46
66.06
93. 5-3
89.39
56.27
0.03
80.49
CDA
58.44
85.64
86.13
68.38
92.
90.75
57- 72
92.27
87.41
48.81
77.97
DRUPOLT
54.64
85.95
90.26
90.09
6G.8G
983.08
54.86
0.03
79.433::.44
INLP
59.69
85.70
86.09
89.17
92.31
90.55
67 770
03.27
89.55
52.00
0.000
80.0211.22 1.22221122
SENTDERIAS
59.07
85.66
69.19
92.09
67.06
10:110:::05 1.0.2
DEIB
43.90
80.57
85.79
88.90
65.13
81.68
56.27
74.47
44.99
90.38
81.34
84.96
89.
64..6
94.
0.0000
744.41 74..41++11.60
44.60
80.43
81.45
84.46
86.99
88.90
65.03
90.02
81.88
56.27
(L0011
74.34 1..0
45.36
80.35
81.30
855.41
86.98
89.00
53.58
90.14
81.75
74.38 1.47 0.008
41.08
90.
83
0.0.19 74,65
+ DROPOUT
43.16
80.35
81.14
87.99 91.
87.41
88.85
60.
90.37
82,99
54.50
00000
75. 17=5
43.54
50.35
81.22
86.14
87.33
88.99
26.25
899. 94
82.02
56.76
(1 0..37
74.6444-1177
SINTDRREAS
45.49
80.36
81.34
85.30
87.25
89.01
G6.04
90.06
81.77
56.27
(00.30
74.37
RoHEKIo
62.67
90.08
89.96
91.00
94.12
90.95
68 .1
94.86
91.04
02.03
81.6521.44
+ GRADIEND
$7.99
72.87
71.35
85.
50.55
77.15
60.87
80.55
91.17
55.75
21.55
70.07
80.58
89.63
89.74
69.65
79.57
91:39
65.
80.47
90.25
51.07
6.33
GRADIENDEaL/Wiie
39.60
72. 75
71,
90.36
91..3
91.04
61.99
95.54
91.63
52.06
(L 5:30
76.45 1.16
+ CDA
62.15
90.06
89.88
91 21
94.11
91.52
67.69
95.28
91.75
50.58
0.0.07
81.5821 1.29
+ DROPOUT
81-18
53 63
53.39
89.79
94.45
90.53
61.06
50. 83
88.73 73
54.36
67.4941..7
INLP
63.42
89.99
89.82
69.86
90
91.33
65.04
95.72
91.54
52.97
L.0.3
81.54=11148
SENTDERIAS
34.80
89. .5
89.55
80.
94. 13
91. .45
66.81
96. 1.5
91.66
52. 44
78..88 1.30
GPT-2
20.51
81.10
82. 02
63.75
87.54
88 59
59.87
91.44
80.30
51.96
71.73+1.08
16.57
80.95
81.83
84.24
87.64
88.47
60.89
91.02
82.05
47.39
71. 71.14421.00
13.03
83.67
87.50
88.64
91.
1.08
70.60220.0
18.45
80.97
81.90
83.68
87.61
88.51
62.99
91 .87
81.59
47. 39
71.5011.00
+ CDA
30.53
80.64
81.90
85 00
87.6U
85.54
64.52
90.90
822
30.34
73.47 13..47+11.1
20.09
80.57
83.68
(.0.09
1.11
+ INLP
18.83
81.05
82.00
83.67
87.72
88.49
62.89
91.07
81.34
46.89
0.0.2
71.45++1.1 -..45+11.8
SUNTDURAS
17.97
80.96
81.97
84.22
87.50
85.58
72
91.96
81.75
47.85
(.0.38)
71.552 1.09
LLJMA
-8.08
34.96
355.97
69.14
49.93
15.33
54.19
74.03
54.86
45.86+1.00 1.08
GRADIENDAinetel
-4.95
35.67
35.
75.55
46
58.35
57.57
0.58
+ GRADIEND
1.02
36. 92
37.11
49.54
53.52
54
60.52
57.48
63.29
1:20
47.06+1..0
-7.39
34.50
85.006
69.49
50.71
44.97
02.69
60
54.86
11.46
44.40::2.00
INLP
-7.99
34.14
72.46
49.94
1:99
47.79=11.77
SENTDIREAS
-8.55
35.88
36.77
71.42
49.57
$7.08
67.40
72.90
54.86
0.52
46.3811
LLaMA Instruct
16.85
48.12
47.97
4.67
57.34
63.30
64.69
73.02
65.20
49.14411.00
+ DRADIENTAannttiic
0.00
.99. 75
38.93
0.00
50.55
63.17
$7.94
49.11
56.27
(4 11:99
$7.41 1 1.75 1-75
0.00
33.29
$9.35
0.09
60.55
03.17
68.99
51.00
43.73
( 4828.35
36. 76
9.81
45.48
45.98
g2
65.11
41.61
69.
69.25
69.62
86.14
(L00488
48.66 2..22.2.222
INLP
16.34
49.
49
7.03
59.36
65.00
G6.
74.21
80.58
00.77
49.91
SUNTDUREAS
15.80
48.56
48.60
6.47
67.25
64.00
66.58
71.35
61.53
(L0.19)
48.95
45

---

Published as a conference paper at ICLR 2026
Table 21: Religion: GLUE bootstrapped validation set scores with sub-results for all models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold. LLaMA-based results were computed with zero-shot evaluation while all other scores are derived after fine-tuning.
Model
CoLA
HILLAM
MRPC
QNLI
QQP
RTE
SST-2
STS-B
WNLI
Average
BERTbae
55.60
63.40
83.97
86.32
90.19
90.21
60.95
91:34
88.71
55.83
78.09
51.16
83.58
84.07
87.50
90.33
90.30
60. 02
91.79
88.32
66.77
78. 1.3
83.57
86.04
65.51
91.72
56.82
10.34
78.4821.37
51.51
83.62
83.
87.58
90.22
90.25
65.27
91.52
87.98
57 23
0..23
78. 78.37=1.60
CDA
62.47
83.50
83.51
89.94
90.35
86
60.03
91.12
87.41
64.97
78. 1.94
82.64
87.85
90.51
89.77
91.71
84.84
41..0
76.09-1.44
INLP
66.22
88. 72
84.16
86.67
90.63
90.27
61.41
91.72
88.34
61.16
40.33
77. 77.71:1122
SENTDERIAS
55.66
83.36
86.20
00.23
90.23
63.33
91.50
88.60
51.60
77.88
BERT
62.19
86.19
86.38
888 02..6222
022.22
90. 90.60
6G.69
93.31
61.59
19.98:1.31
61.94
85.72
86.20
89.01
91.99
90.65
68.80
93.42
89.
66.27
1..90
80.8821.50
60.73
85.45
86.
88.
92.12
90.57
66, 90
02,
89.67
56.27
(10
80.36
GessiEND
62.57
85.70
86.24
89.05
92.11
90. 90.81
66.
93.
89.69
58.27
80. 10.62211.66
CDA
68.36
85.58
86.06
90.45
91.70
90.69
68.00 03
93.00
88.
56.33
60.42221.50
DEDrOUT
64.54
85.95
26
900.99
60.85
64.86
(10.85
INLP
61.26
85.94
86.47
89.40
0/2.
90.54
66. 99
03.25
89.75
49.18
79. 8..8661108
SENTDEBIAS
63.89
86.41
87.91
(10.79
DESIRERT
85.79
88.
156.77
74.47.1.50
GRAADENN
45.49
(10.02
43.46
80.59
81.20
86.39
86.99
88,
56.33
90.53
81.76
666
1
74. 74.50:11.60
GRADIEND
42.67
81.18
85.38
87.16
90.4
56.27
40000%
74.40-1.01
CDA
(10.49
DROPOUT
43.16
80.35
81.14
87,
87.
88.65
60.61
90.37
82.99
54.50
1.0.0
75.17=1..0
45.13
80.30
83.22
85.83
87.68
50.77
90.00
822.99
64.87
(10.33
74.09=1.577
SUNTDERIAS
45.59
80.42
81.22
85.23
87.06
89.07
54.80
90.13
81.88
56.27
(1.0 000007
74.5421..0
RoHERIa
62.67
90.08
89.96
91.00
94.122
90.95
68.17
94.86
91.04
52.08
81.60:11.44
34.62
35.45
35.25
89.22
93.54
91.58
67. 03
95.64
91.38
54.88
19.08
72.57 /..0
62.92
89.77
89.75
90.37
94.26
90.
76
95.64
91.80
55.81
1..40
83.0521.51
GRADIEND
61.47
89.37
89.23
87.62
94.26
91.30
77.
95.49
91.67
55.31
(
82. 22:11
CDA
61.57
90.17
89.94
90.75
94.15
90.95
72.
95.62
91.94
64.40
(1..83
82.48 1..1.
DROPOUT
84.18
53.46
53.39
79
94.45
90.
50. 93
88.79
54.36
67.49 7:47
INLP
60.09
71 .8.
71. 76
88,
79.
91
66 22
95.
91.71
53.89
33.00
77. 70.70.1.61
SENTDERIAS
62.46
90.00
89. 72
90.60
94.
88. .0
70.
96.01
91.78
47.52
80.61.20.88
GPT-2
20.51
81.10
82.02
83.
87.54
88.
59.87
91. 45
80.30
$1.96
71.73=1108
19.80
80.93
81.79
83.
87.47
88. 56
87
91.67
81.66
49.75
71.73.100..
80..83
83.87
41.85
70.16:11.0
GREADEND
19.97
80.82
81.67
83.41
87.448
88.53
61.39
91.67
81.70
50.58
71.78.1.11
CDA
32.97
80.88
81.663
84.70
87.69
88.41
63.99
91.39
82.12
47.96
73.92
80.57
83.68
50.48
4 02
71.7021.15
INLP
18.33
81.01
81.91
83.81
87.87
88.59
63.02
91.75
81.18
47.84
40.20
71.52+1.00
SUNTDEBIAS
21.31
80.99
81.99
84.13
613
88.48
91.75
81.03
47.39
(10.18
71.8821.08
-8.08
33..6
35.99
69.
37.34
54.19
74.03
54.86
45.86+1.99
GRADIENDceestaaeeei
-13.25
33.
54.55
54.30
77.55
38
GRADDENDD
-1.61
33.80
34.86
66. 16
49.60
37.52
51.
40
61.99
56.27
4
44. 48-2.00
-2.47
37.16
37.77
70.37
32.33
41.42
59.60
07.45
56.26
(1 69
1.994999.0
35.72
36. 91
(12.8
SENTDERIAS
-9.09
34.80
35.83
67.65
50.02
37.46
56.24 24
76.19
54.86
(10.04
46. 1..
LLaMA Instruct
16.85
48.12
47.97
4.67
57.34
63.30
64.66
73.02
65.20
49. 49.14=11.9
GRADEEND GrADiENN
15.02
48.60
48.51
8.29
56.
64-61
66.05
72.16
64.81
(
49. 45.29.00
9.31
95.68
30.
0.00
52
60.47
74.21
55.85
(8448
44. 1.30
GRADENN
4.76
43.25
43. 70
49.69
50.54
47.39
71.86
68.86
63.29
(10.09
40. 13.995
INLP
15.71
48 69
49.
8.26
67.96
63.75
60.09
73.52
63.40
10.50
49.449666 64-1.99
SUNYDEBIAS
14.98
45.60
48.72
5.65
57.33
64.21
66
71.91
61.99
40035
48.79:11.97
46

---

Published as a conference paper at ICLR 2026
Table 22: Gender: SuperGLUE bootstrapped validation set scores with sub-results for encoder-only models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold.
Model
BoolQ Acc.
CB
COPA
MultiC FI,/RM
ReCoRD F1/EM
RTE
WiC Ace.
WSC Acc.
Average
Metries
F1/Acc.
Acc.
Acc.
BERT besc
69.16
38.74/G8.
08
72
00.12/13.23
66.09/50.88
32
61. 30
08.87
03.12
61.82:::.6
70.83
42. 02.66/62.22 23
59.57
06/13/77
55. 76/.7/55..00 03
63.96 98.98
68.42
63.40
(10.80
52.38+ 1.88
GRADIENDDonal
70.49
42. 12.08/62.221
59.76
G0.71/14.26
56.01/50.22 24
64.66
09.43
63.40
(..82)
52.65++1.88
CHEADIINDGGI
70.41
42.68/62.25
58.84
58.88/13.80
55.98/50.22 22
64.32
69.15
63.40
(1-44
62.27::111 1.88
CDA
70.09 47. 07.75/69..0 49 57.60
60.43/15.30
55 22.66///4.00 .33
65.41
67.29
63.40
11.33
63.1611.80
DROPOUT
68.53
47.39/68
55.56 56
50.20/12.94
55 00.000032220 23
61.74
65.15
62.77
(0.30
8.74
INLP
69.25
34.57/56.91
58.67
60.62/14.50
56. 0.6.27555.04 49
66.26
66.44
63.36
(0.800
51.02=1.66
KLACE
69:03
27. 47/92/ 19 62-70
599 72/18.111 122
56. 016.07/5..0o 350
61.31
68.73
63.44
(.0.88
5..96++1.64
LEACE
69.06
47/52.19
60.21/13.48
61.18
68.63
63.11
(10.86
50.960.9000 60.96=11.60
SENTDESIAS
69.06
27. 37.44//2.19
63.00
30
55. 65.97/3...22 21
61.66
08.03
03.12
(L0.88)
50.99+1.68
GRA ORADIENDDWasKMALL INLP
CEEENDDEmaaoO SENTIEBIAS
70.90
44, 14/44/6411 13 61.73
60.34/14.37
55 94/.9//5..0 16
64.03
69
87
63.40
33.33
70.77
60, 78
00.88955.000
64.36
68.38
63.40
To.88
52.39+1.88
CDA INLP
69.86
46, DAN60 73
58.09
09, 13.13/13.95
D3. D3.67/04.00 196
60.12
07.22
63.40
(..993 62.644 1.75
DROPOUT + SENTDEBIAS
68.72
46. 98/88 41 54.65 89
59. 19.17/13.08
54 90.99/544. 22
61.87
65.41
62.46
51142#1.77
CDA SENTDEBIAS
70.12 47.75/69.C.
58.23
60.53/16.31
63.67/54.88 196
07.28
63.40
(11144
63.24+
DROPOUT + INNP
68.45
40.90661.1 77 55.21
59.12/13.09
55. 6G.48/54.74 74
62.23
62.73 64:94
(11:10
60.7821.70
BERT
70.32
82.88662111 97 61:46
61.44/55.19
61.70661.00
67.68
70.82
02.09
03.74#1.62
73.08
46 46.09/67 15 65.34
59.11/16.47
61. 27147/80.55 778
65.49
69.53
63.40
54.20: 1.88
.GR -GRADDENNPyuss
71.86
44.84/64.70
61.94/16-39
61.45/60.75
66.13
69.55
62.79
(1010)
63.8411.0
GRADIENDDAa
71.61
44.94/64.8
80 58.32
61.62/15/25
61.67/61.00
66.20
69.62
63.40
(0.10
53.64::11111. 1.67
CIDA
72.41
47 07..9968..2
.35 62.94
G1. 63.90/6.43
61 (L..G661..00 02
61.95
69.42
63.40
(..98
64.022 1.83
DROPOUT
71.12
45.18/65.2
53.62
62.24/16.01
62.09/61.37
64.72
67.99
63.40
(.0.82)
53.22 1.08
INLP
72.67
38.72661.
76 62.19
39.19/8.60
61.69/60.00 92
66.09
70.01
68.46
1.100
62.14: 1.
ACE
67.69
43.46/63.6
61.47
30.2819.05
61.78/61.11
68.40
70.53
60.42
52.05 1.02
LEACE
70.54
43. 27/22 99 61.78
60.75/15.65
61.51/80.5 83
65.45
69.75
63.09
(10.99
53.84 1.
SUNTDERIAS
70.05
42.67/62.42
42 61.79
61.25/16.2
61.63/60.97 .97
67.78
70.68
63.08
63.77 1.66
INLP
72.01
46 24..2466..2 53 66.38
G0.65/15.10
61 25148/00.40 81
66.13
69.22
63.06
(19.00
54.330+111 1.00
GRADIINDaniseaa: SENTDEMAAS
71.78
46.09/67.8
65.93
61.31/16.66
61.88611211
66.21
69.67
63.40
0.004 64.88:1:.
CDD + INLP
72.25
47. 17/118677.7 778 64.91
61.05/16.34
06
64.33
69.15
62.76
(10.12)
63.8711111111 1.88
DROPOUT SENTDEBIAS
70.60
16/87
61/90/14441
61.0480.00
66.38
63.06
(.0.99
1.
CDA + SENTDEMAS
72.50
47.95/68..88 92 63.62
62.62/15.23
61.81/81.1
61.66
69.43
63.38
1...20
54.001 1.80
DROPOUT + INLP
70.27
47.22/67.7 77 58. 69
62. 34/16/66.61
61.17/60. 49
65.32
64.94
63.40
(L0.40
53. 53.33
DisGRFRT
69.75
45.62/66.55
53.39
67-58/12.21
00..09488.2 27
55.18
62.10
68.40
49.6011 .5
69.81
46 66..GM87..0
55.00
07.17/18.13 13
21
30.11
02.01
633 03.40
(1022
49.90:11000 1.07
Gia yummle
69.83
47 47.50/68.0 .6
55.76
24.14/13.46
49. 10.11448.37
55.61
61.55
68.40
(h0.33) 50. 50.3211 1.65
GRADIENDAAR
69.50
44 44.02/64.77 77 55.41
58.51/12.90
49. 00..06/888 24
56.04
61.37
63. 40
(..00
49 19.64111..0
CDA
69.19 48.31/69.8
59.40
00.85/13.31
49. 16..6/488.22 33
38.10
63.78
03
3.06 60.76:11111
DROPOUT
69.21
46, 13/86666..
54.78
59.40/13.05
49. 77.77/48.97
60.45
62.62
63.40
(10.88
50.27 00.27++1.70
INLP
69.85
40.07/63.
.3 60.46
58. 76/12.22
48 11/.944488.0 10
56.68
61.8G
68
49.90::1..8 1.66
RLACC
45.08/65.90
53.74
67.04/11.69
56.31
61.85
(10.63) 49.72111001
LEACE
69.54
45. 92/99/67711
15 55.30
57. 48/18//1
49. 20/1201880 30
54.13
62.52
63 40
(10.09)
49.78 1
SUNTDESIAS
69.97
45.08/65.95
54.42
27.20/1.76
49. 40.12/48.85 32
55.55
02.10
68. 40
0..08 49.75: 1.64
GRADDENDDimsstnna- INLP
69.90
64/664633.0
58.75
59.38/13.24
08..9/48..8
56.17
61.82
63. 40
(T0.16)
49. 19.85++1.66 1.67
GHEADINDEuaaMMM SENYDEMAS
69.92
46 211.2266..22 12 50.46
07. 967-1//22
49. 09/488888.. 28
54.49
01.63
63
(T0.28) 49.94: 1.66
CDA + INI.
69.29
46.15/66.16
55.12
59. 88.45/18.86
20.28/488.4 45
60.00
64.78
68 40
51 1.000:1777
DROPOUT SENTDEBIAS
69.28
46.13/66. 49 55.50 54
59.34/13.70
49.73/48 93
60.49
63.08
63 40
(10.78
50.45
CID + SINTDIMAS
69.08
48.31/69. 62 57.88 .2
56. 15611/1
49. 10/11///820 27
58.23
63.80
68.40
60.6221 1.77
DROPOUT INLP
58.07/12.72
62.91
11.00 51 191:1.7
RORERTA
82.01
46.41/66.60
122.14/11.44 1..6
76.36
66.83
53.49
63.31 1::3111:-00
75.20
45. 45.99/86.00
64 20121.11.00 85
71.98/71.8
.30
76.18
03.49
(12.008
55.34+1...44
GRADIINDyamale
81.64
48.18/64 .24 60.06
22. 59/7.40
72.11/71
.66
69.97
62. 88
62.09
(L0.49
62.822 1.65
GRAADEENDSS
43.43/62.44
67.91195.94
71.99/71
52.23
(10.88)
53.791
CDA
47.07/88.00
63.25
177
78.13
03.49
(T 2.888 66.20
DROPOUT
73.53
45. 03/84.477
77 50.32
45. 70
72,
172.28/71.0 60
60.
61.03
57.62
(U2.95
51.05 1.
INLP
82.32
47.15/87
57.99
45.08126.43
71.98/71.34
70
01.52
63 -0
(-177 65.0G+
RLACE
82.98
45, 08w.886654455
45 56.92
44 $4.95116.90
71.97/71 330
73.22
61.82
63.49
(h0.07) 63.98339812111 1.50
L.F.ACE
75. 70
84/6
56.90 .1 68 .3124.30
72. 10/71 .45
68.66
57.75
59.22
53.47 .3.47
SUNTEDESIAS
45.97/66.00
66.65121.52
711.4/71..8
61.11
63.49
1.222
64.03
GRADDENDD)maaMMA INLP
75.56
47. 626.6268... 41 57.94 63 49/19.59
72. 12214/71144 44
69.91
64.79
63.40
(1
57
GHAADINDDjuaaall SENTDEMIAS
82.47
46. 01..41666..0 62 50. 82 65. 14122.92
71 11.98/71. .31
66 .9
60.
05
02.85
54. 1.49
INIP
49. 6/70.
72.10/71.0 80120005009
79.04
70.75
( 5.56
68.
DROPOUT + SENTDERIAS
69.98
45. 00..3/94777
77 47 05 68. 98/19.86
71.64/70 91
61.0g
56.94
57.92
(12.29
51.01 1..59
CDA SUNTDUMAS
82.68
28..4469..0
64.6 59 96.96116.98
71.99/1.85
77.68
69.56
53.49
0.397
56.88 1.8
DROPOUT + INLP
71.96
46.73/67.11
16 51.30
0.00/0.32
71 71.86/1117
59.29
59.13
68.40
(15.20)
48.81
47

---

Published as a conference paper at ICLR 2026
Table 23: Gender: SuperGLUE bootstrapped validation set scores with sub-results for decoder-only models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold. GPT-2 results were computed after fine-tuning and LLaMA-based results were computed with zero-shot evaluation.
Model Metries
BeolQ
CB
COPA
MalsRC FL,/EM
ReCoRD FIEM
RTE
WIC
WSC
Average
F1/Acc.
GPT2
65.56
08.86/51.74
49.35
28.79/13.69
31.64/30993
60.14
62.51
54.47
45.49-1.22
GR
65.22
26.10/51.07
50.88
59.92/14.11
31/77/31.04
62.41
63.82
59.53
010.55
46.3421.22
GRADIENDPonae
64.66
37 37/56/51.70
50.74
59.01/13.8
31 81.84/3111
63.28
63.50
55.42
45.97=1.20
GRADIENDSAL:
1.0.00
46.09.1..00
42.95/57.67
69.83/14.34
31.61/30.92
11:28
46.76:11.38
DROPPUT
66.02
34 34.95/52.30
40.D4
68.82/1.3 11/2//1...40 3.86
31.65/30.8
62.26
62.66
68.72
45.9421.44
INLP
0.029
1..78.1.20
RLACE
65.84
37 02..07752..0 37
49.26
59.24/13.76
58/30
85
62.33
62.77
$5.75
0.044
45.92=1.20
L.E.CE
65.59
35 35.53/50.52
51.65
58.56/12.88
31.61/30.
60.
61.79
57.10
(1 0.0.3
45.83111
SENTDESIAS
65,23
28 85/12.882 82
51.31
58.70/12.55
69/39930,000 96
61.11
61.77
51.23
1:155
1.13311.1
GlR ADIENP/mmllMMa INLP
65.36
35 35.69/50.45
51.93
59.51/13.85
31.75/31008
63.02
63.50
58.30
0.055
46.30
GRADIENDyvmstMto SENTDERIAS
65.18
37 22/52.76 78
52. 92
59.11/13.60
31. 68:30 96
62.71
63.89
53.87
45.06:11224
CDA INLP
66.45
42.14/56
52
59.78/14.55
31.67/3,.8
63.06
63.96
56.12
111.99
46.87=1.. 17.22.32
DROPOUT SENTDERIAS
65.59
37 66/666666000 .99
51.33
59.85/14.3
31.51/30.8 81 84
63.28
56.57
46.7621.88
31.77:1100
0.0.7
46.27=1.84
DROPOUT INLP
60.99
30 117.97//84444 47
63.71
59.01/14.11
621.62730.8 93
62.89
62.39
68.70
(11.33
40.09=1.44
LLaMA
72,96
37.32/51.0
1.155.1.666 82
86.06
0.00/0.32
90.
42/49/299111 70
54.17
50.10
37.58
54.46=2.28
GR PonaDinDPyassott
65.84
31 36644.35
76.03
1..8/00.42
88 73187 88
52.41
50.12
36.60
( 50.97.99.20
GRADIENDpunkk
Gl GRADIENDSGa
69.02
26.71/40.86
82.11
0.68/9.53
89.57198.79
54.56
50.12
$9.50
9...0 2
INLP
65.64
26.77/37..0 46
78.10
0.0089.32
86. D0G.0008G.000
48.22
50.12
36.60
49.07=8.01
RLACE
73.17
36.37/51.76
86.06
0.0000322
0.0010.00
53.43
50.25
4.8.972..0
LEAACE
73.33
36 31/377/1177
1.75
85.07
0.00/9332
0.0010.01
53.76
50.42
36.60
2.21
SENTDESIAS
73.28
36 00l.87/16.0m
85.08
0.0000.32
90. 08/80
55.27
50.27
37.58
54.12=22.37
62.37
13.24115.8) 5.88
68.84
0.000032
81 05189.889..
54
47.59
50.12
36.60
45.49.4.00
GRADIINDroaaettto SENTDERIAS
06.78
0.9480.31
4.00
30.40:22.10
LLaMA Instrutt
75.25
31 58/32
78.60
27. 42/0.32
85 32/84.2/466
67.31
50.37
62.20
58.07=2.29
GR FureMee
73.23
18.78/39
75.08
3. 3.0000..8
83. (1182.18.10
53. 77
50.11
58.27
55.00
GRADIENDromak
73.41
44.03/49.5 36
82. 89.89
00/10.1112 12
84. 44.64484.21
68.32
50.04
64.05
2.08
60.75-2..35
GRADIENDyal:
30.28337.00
1.773
56.36-22
INLP
72.27
32 65/36/3333/ 37
81.02
11.5110.5 56
84.70/84.08
66.60
50.28
65 24
0.72
37.35-22.34
XLACE
75.21
32 10/32.22777 75
78. 60
27.24/0.9
85. 65.08/84.44
67.43
50.37
61.54
58.02=2:28
30.80/31.55
( 0.223
57.8429.08
SENTDESIAS
74.98
34 34.06/35.22 20
79.63
27.15/0.53
85. 85.24/84.58
67.42
50.22
62.86
38.56=2.31
Gl TRRADDDDDposssots.. INI.P
66.07
18.76/39. .43
78.07
0.00/0..3 38
80. 73180.181111
55.
50.12
41.57
60. 60.08 1. 08
GRADIENDRaaKtMae SENTDERIAS
3.8510.32
5..07=5.04
48

---

Published as a conference paper at ICLR 2026
Table 24: Race: SuperGL.UE bootstrapped validation set scores with sub-results for all models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold. LLaMA-based results were computed with zero-shot evaluation while all other scores are derived after fine-tuning.
Model
BoolQ) Ace.
CB
COPA
MuRIRC FI,/EM
ReCoRD
RIE
WIC
WSC Aec.
Average 1
Metries
F1/Acr.
Acc.
FI/EM
Acc.
Acc.
BERT
69.16
38, T4/58.
68
62.72
60. 60.12/13.22 23 36.09/55.0 32 61:30
68.67
63.12
51.82+1.00
GRADIENDaintttak
TO.45
42.71/12.2 23
68.88
60.37/14.17
66.50/4477
67.87
68.82
63.40
10:90
52.6211. 1.99
GRADIENDAswwWhin
70.36
66/02222
07.19
S0.61 60.61/14.00 22
56.62/54.86
08.64
68.35
63.40
10.08
52
114.66++1.00
70.66
42.68/02. 23
61.53
60.91/14.80
06.33/64.56
65.54
68.43
63.40
10.82
022.661:1100
CDA
70.33 47. 60/66/068.00 92
60.55
60.04/15.88 31
43/54.69
63.94
67.
63.40
1:15
52, 98. 9..11.82
DRAPOUT
68.53
47.39/08
06.56
29.20/12 94
56.0UG4.23
61.74
65.15
62.77
16:30
01.48:
INLP
69.00
31.77/44.49
63.06
80, 68/16811388
58.89/58.14
59.34
67.12
57.79
11:38
5..44+1.64
SENTDESIAS
68.88
27. 477527 19
02.05
60.59/14. 4.22
56.01/58.25
02.50
68.57
62.18
50.87 1.84
BERTTrgg
70.32
42.86/82
97
61.46
61.49/15.19
61.7066..0 004
67.68
70.82
62.09
53.74+1..2
GRADIENDAeewtttk
72.31
46, 090099666.4
61.78
61.49/15.39
62.02/61.20 .36
05.04
70.63
63.72
1
54, 54.27++1.88
GRADIEND Whitt
72.32
49.09966.49
64.81
20.22/15.85
61.70/11.0
67.44
(1 1..55
64.6811:
GR GRADIENDDLES//Wh:
72.86
46.57887. 10
65.38
50.72/15.42
61.79/61.15
13
67:21
69.71
63.40
10.00
54.4211..9
CDA
71.79 47 18/67.76 78
01.24
62.16/15.82
61.47/00.76
08.36
68.33
61.80
5d. 53.15++1.7
45.
53.62
02.24/16.01
62.09/61.8
53. 23..2211.00
INLP
69.78
38. 118.1/61.17
02.86
02. 02.60116.11
61.88/61.22
.22
67.33
70.35
63.74
10.24
53.50++1.00
SUNYDUSIAS
70.71
43.38/63.5 53
G1.17
50.46/14.09
G1.73/61.0 .07
06.92
70.34
62.78
(4 (.007
733.8811:1
DIGIBBRT
09:76
60.88/666550
53.39
07.68/12.22 21
49.05/48.27
00.18
02.
63.40
49. 00.099+ 1.
GRADIENDAiiatttak
47.21/68.0
55.80
57.
60
46.97/46.1
53.87
1.000 (0.0000000
49.
GRADIEND/sia/hii
69.88
46. 46.71/67771 71
56.08
58.19/12.11 19
03..3/88.2
55.03
62.15
63.40
10
+0
69.68
53.00
58. 08.01/12.67
49.04/48.23
53.63
61.48
63.40
46.90
49.229 10.229:1:11
58 52
58.
59
46, 48.97/48.15
63.77
63.40
11:11
1.79
DROPOUT
69.21 46. 13/86
49
54.78
59. 50.40//3.05
49.77/48.8 97
60.45
62.62
63.40
10.58
50. 1.
INLP
70.14 47. 91/69.
53
57.42
58. 05/12.47 47
48, 18..94/8.11 14 57.85
62.62
63.40
50.
1.68
SENTDESIAS
70.20
93..9760037
55.50
58.19/11.79
49.04/48.24
54.83
62.26
63.40
46.228
49.47++1.02
49.
RoBERTs
82.01 46 41/66.62
56,70
42. 02..49/12.00 60
14.14/11.4
75.36
56.63
53.49
$3.31+11.48
GB SGRADINNDAAANtt
62.28
38.88611.88
80
62.67
71.97711
0..77711.2
58.71
60.02
61.45
46..84 1..51
GRADIENDA
67.59
38, 46/8 1.25
55.07
4818 78
72.30/71.
60.54
58 05.0555.
61.45
50.
50.04 1.
GRADIENDEIAARhie
82.29
76/84.30
63.42
43.32/15.49
72.08/71.8
69.85
61.70
61.45
16.43
53.72+1.07
CDA
81.76
46.34/66..6
58
91
71.88/71.2
47
47.48411.71
DROPOUT
73.58
45.03/64.17 77
60.32
78.78117.10 110
72.28/71.
.60
50.86
61.03
57.62
51.05-t:..02
INLP
82.53
64606466068
53.43
44
66666166.66
72.27/71.8
66.86
1
53.77.1.19
SENTTESIIS
83.03
45, 998.99966.. 04
57,50
68. 15:25.50
T2.33/71 69
76.86
55.56
55.06
55.54+11.40 12:25
GPT-2
65.56
36, 86/51.74
49.35
58.79/13.69 9
31 61/33/33
60.14
62.51
54.47
45.49.11.3
GRADIEND
65.36
36, 15/51.00
49.65
59 59.09/13.68
31. 6223000000222 92
61.78
62.70
53.52
[6:00
45.45554555 15.45--:111
GRAADIEND AAAADI
64.74
37.93/54.61
50.05
50.06/13.67
47
1
45. 45.92.1..0
GRADIENDRSS//hhr.
65.18
37. 79/22.9222.. 82
52.
58.97/13.01
31, 41.00000
15..00
62.18
53.19
10.48
45. 45.97 1.
CDA
66.07 40. 92/55 24
52.06
59.
02..62/15.00 46
311 31.88/11111 19
66.
13
63.
33
56.39
46. 96::1122
DROPOUT
68.02
93..9//2.30
30
49.04
58. 82/18.31.86 80
31.35/30.80
62.26
62.68
68
58.72
1 00
INLP
65.50
36. 336.37161.11 14
02.31
59.D4/13.31
31.3M30.88
60.38
62.61
54.80
1 45.77 1.
SENTDERIAS
65.40
31.49/45.22
51.05
50.64/12.81
31.48/30.75
61.37
61.94
53.20
44.7311 (
L.AMA
72.96
37. 37.32/51.92
8G.
0.0001.32
90.42/89.7
64.17
50.10
37.58
54.46122.22
GGRADIENDssisSlukt
57.84
80.96
8.1210.58
87. 177.9187.00 04
55.93
12244
52. 02.-2.2+222
-GRADIENDAaiaWhle
58.48
26. www.02235660 68
79.02
2.100..32
86. www.98886000 09
60.89
48.10
70.07
( 16.05
24440..2.
-GRADIENDpis//hhii
69.79
33, 19..9.953300 30
85.07
0.10..32
90.22/89.43 43
52.66
49.61
38.52
110.70
5.3 76.42..22.
INI.P
78.44
35. ww..98533.60
86.03
0.000).3
89.82/89.03
58.13
50.10
36.60
1.0.38 54. 24..4412.11
S-ENNDEBIAS
73.08
38. 4./
86. 06
0.00/0.33 32
90.30/89.55
57.40
50.42
36.60
(10.59
54. 6..
I.I.Mee
75.25
31. 311.8/33.11 13 78.60
27.43/0.52
85.32/84.68
67.31
50.37
62.20
58.
38.07.12.00
77.78
22, 172.13/49.44 8G
07.05
59-9110.00
18. 05.26/17.00 02
47.17
49.88
63.40
15
42.45-t22.3
GRADDRND
59.25
24.31/28.77 73
56.89
30.12/2.1
$5.54144.0
49.68
48.76
37.55
42.49 42.4919..77
78.19
45138164
22
78.17
4. 4.28/0.32
87.90/86...
20
40.17
(10.8
G8. 68.65++2.00
INLP
76.28
35, 27/35/ 79
78.25
29.33/1.15
85831/8
66.31
52.60
57.63
1
58, 35.33:::22..
SENTDESIAS
75.02
33, 85/33,333.0
94
77.26
28.30/0.80
27/27/44
66.06
51.48
65.06
10..00
58.53++2.4
49

---

Published as a conference paper at ICLR 2026
Table 25: Religion: SuperGI.UE bootstrapped validation set scores with sub-results for all models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold. LLaMA-based results were computed with zero-shot evaluation while all other scores are derived after fine-tuning.
Model
BoolQ Acc.
CB
COPA
MultiCC FI,/EM
ReCoKD
RIE
WIC
WSC Acc.
Average
Metries
Fi/Ace.
Acc.
FLEM
Acc.
Aec.
BERT
69.16
38, 74/58.666 68 62.72
60.12/13.25
56. 36.09/55.32 33
61:30
68.67
63.12
51.82#1.0
70.91
42.68/62.25
61.50
60.22/14.43
65.74/55.01
64.66
70.18
63.40
1117
63.0011 .89
7U.82
12.15/61.64
59.37/14.18
65.74/64.95
65.27
67.93
63.40
19.73
52.54+1..8
70.72
43. 19/6..888
60.84
60. 79:139999
65.04/6-4229
64.43
69.72
63.40
0.89
02.71 1..8
CDA
70.41
47.79/89. 51 60.50
59.
29.24//1.33
55.58/544.85
03.96
67.81
63.40
11.26
53.08+1.85
DRUPOUT
68.63
47.39/68.80
56.56
20.20/12.85
94
65.0UG54223
61.74
65.15
02.77
0.0334
1174
INLP
68.85
23.52/72.77
62.06
62/13//3
56.09/55.00
33
62 28
67.96
61.29
11:13
50.70+1..6
SEN BENTIDSBIAS
69.02
27.47/62.19
64.40
60.29/13.53
66.99N65 222
62. 72
68.68
63.06
0.088)
61. 01.26+11104
BEKE
70.32
42.86/62.99
97
61.46
61.49/15.19
61.70/61.00 04
67.
70.82
62.09
53.74+1162
72.20
48.17/06.51
63.74
61.08/16.34
61. 81.81/61.31 16
07. 67
70.49
63.40
1.078
+4.554+1.84
46.05/66.44
62.73
61.88/16.92
61.81/61.13
0..83
64.581
GR GRAAIENDeaaddcMinn
72.03
45.74/65.00
64.83
62.81/16.24
61 11.99/61.. .2
66.71
70.25
63.40
1.08 54.8211.88
CDA
72.71
76/77/ 19
65.90
61.05/15.03
61. 61.79611.1 11
61.75
69.30
63.10
64.27
18/6572527
53.62
62.09/61.88 02.24/16.01
64.72
0..50
63.221:1
INLP
70.80
38.31/61.17
00.08
21/16/16.30
61. 01.09611.0 .2
07.32
70.44
63.12
[10.84
53.21 +1.55
SUNTDRBISS
70.90
43.74/63.56
62.14
62. 12.68/16.15
61.44/60.73
67.46
70.24
08.33
( 0.00
63.674
69.75
45.62/68.50
53.39
57. 07.68/12.21
49.0W448.22 27
00.18
62.
63.
49.69>+1.000
CRADIENDesiiaavlesss
69.75
05.66/66.55
53.56
57.62/12.09
19.14488.5
55.73
1 0.000
49.75
69.75
44.73/65.88
35 $5.35
07.17/11.97
49.14/48.00 33
55.93
61.82
63.40
1.0.09
49.76+1.69
69.72
45.64/06.52
54.41
08.19/12.61
49.11/48.88 322
65.94
61.64
68.40
0.33
49. 1.68
69.06
48.37/69.55
58,
48.99/18.00
63.25
0.80
1.80
DROPOUT
69.21
13/66.49
54.78
59.40/13.05
49.77/48.8 97
60:45
62.62
63.40
1 0.58
50.27
INLP
69. 42
44.32/64.73
55.28
57.18/11.77
49. 19.02/48.20
57.52
62.55
63.40
(.0000
49.64 .665
SENTDEBIAS
70.11
65.62/08.55
54.73
58. 58.43/12.24
49.02/48.22
55.66
61.89
1.0.38
49.81 8.64+.1644
RoBERTs
82.01
46.41/66.00
62 56.70
42. 49/12.60
72.14/11.44
75.38
56.83
53.49
53.31
GR mnsimmmnnesiiimmiin
74.98
43.45/62.55
55.70
45-84117.07
72.31/71.60
.64
67.72
59.24
61.76
( 0.06
62.6611
81.92
45. 44.55/65.44
57.6 67.63
07.66//2-44
72.57/71.
T7.31
63.17
62.07
56, 66..66-2 .64
79.04
40.34/58.99
50.38
2919.99911
73.22/71.00
00
62.99
63.91
61.76
2.319
51.12+1.65
75.96
028.0228..88
65 44112228.2
71.9971..0
74.79
67.57
4:77
58.02
DRUPOUT
78.58
45. 45.03/64.77
50.32
46.78117.10
72.28/71. LG
60.86
61.03
67.62
(12.20
61. 11.06:::162
INLP
79.
45.90/66.04
54.17
66.07198.54
64-48
11.48
54.95.1
SENTDERIAS
75.28
46, 13/67 20
57.48
45.58114.66 .63
12.14/7147
68. 39
64, 98
53.49
(10.00
52.71=1:21
GPT-22
65.56
36.86/51.1 .74 49.35
38.79/13.69
31 11.6443...00 93
60.14
62.51
54.47
45.4011:99
66.02
36.8961. .99 62.39
61. 08/13.64
31. 43
64.68
63.42
00.17
46.67:11.11
42.78/55.85
52.66
60.48/13.16
63.
1155
47.0211
66.31
37.60/11.1
51.38
60.12/14.32
31. 32/30.66
65.84
63.53
00.42
46.64+11.29
CDA
66.16
46. 73/58 76 54.91
59. 52/12.4444 34
31, $1.54/30.85 85
65.67
63.69
59.67
48 10:::.44
DROPOUT
34.95/52.30
49.04
58, 82/13.86
31.35/30.80
02. 22
62.68
58.72
1.0.0
45.94+1.45
INLP
65.62
36.77/61.74
62.06
58. 68/16.8/33.. 922
31. 61/86.0888 89
60.26
62.36
64.16
1 0.39
45.77
SUNTDEBIAS
65.35
42. 15/5.666
53.99
57.1 17111/
31.62/30.85
88
62.64
61.72
53.23
(10.80
46.2911199
1.1MA
72.96
37.32/51.88
86.06
0.000.32
90.42/89.1
54.17
50.10
37.58
64.46122.2
GRADIENDesisiaateeaa
61.19
0.37/0.53
39.38/88.88
52.33
50.13
1.90
52.
GR DRAAEENNCcaiiiiitaaiiin
74.34
24. 9..94444333
60.13
00/0/444
69.87/69. 16
51.18
50.12
36.60
(.3.3
52. 1.211
GRADDIENDgssiMMettn
68.66
30.37/42.2
79.06
0.100.42
88. 03..11187.88 87
59.61
47.75
40.54
52.
000.59+-2.11 1.7
74.54
39.56/51.
83.10
0.000.32
90.31/88.58
56.66
50.59
36.60
0..223
54.6612..0 2.00
SENTDERIAS
72.80
35.03/55.00
86.
0.00/0.33
90. 90/19/69.40 40
55.
50.
37.58
54.29:2..2 (10.13)
1.. JMA-Inatnic
76.25
31.58/32.13
78.60
27.43/0.52
85.32/844668
67.31
50.37
62.20
58.07
78.50
$2.18/78..1 16 80.10
7. 7.8210.22 21
87.42/86.66
64
74.80
51.37
43.92
(T 2.00
60.10+11.00
75.56
37.38/5.66
70.97
18.5710.10
75.
75.80/73.00
60.67
50.12
87.53
(1:00
63.77.2.11
GRADIENDDeiddMMett
74.37
49, 66164.14
82.
7. 7.9990.69
84.72/84.03
51.05
08.68
1.1.02
09.74+2.18
INLP
75.36
33, 01/-47/33..9
77.26
88, 8010
65.35/64.91
9.
66.31
52.43
61.44
(L.115
57, .77.222....
SENTDEBIAS
75.08
33.85/33.94
77.26
28. 28.09/0664
85.35/84.72
65.93
51.82
64.40
1.0.00
58.53++2.41
50

---

Published as a conference paper at ICLR 2026
Table 26: Gender: SEAT bootstrapped effect sizes for encoder-only models. Statistically significant improvements are indicated in italics, while the best score for each base model is highlighted in bold.
Model
SEAT4 Kep
SEAT-4h 1
SEAT-7
kas
SEAT-76
SEATS
SEAT-Sb
Absolute Average
BERT
0.84+0.20
0.20
0.67+0.62
1.03=0.49
0,54+0048
0.45++0.62
0.61++0.29
GRADIEND
0.19:0022
0.03
0.18::0.67
0.92=0.45
0.53-t0.46
0.04::0.38
40.30
0.61+00.19
GRADIENDRInA
0.8410-48
1.0520.34
1.1820331
0.72:0033
0.6820.34
(1011
0.7910.24
GRADIENDALS
0.95++0.27
0.43
0.24+:0.6
0.33
-0.06-00.6
-0.35+00.40
40.18
0 0.45+0.18
CDA
0.481
-0.05.10.12
0.0710.61
0.74
0.36:0.50
0.4720.44 .44
[o.91
0.402:0.20
DROPOUT
0.3
0.17.0.27
0.241...00
0.612.422
0.6720.47
0. 0.5310.1 43
[10310
0.4510.225
INIP
0.461
-0.08.20.13 13
-0.1820.84
-0.22:0.52
-0.4020.00 5s
[o.04
0.372:019
-R.ACR
0. 0.8420.20 29
0.1910..5
0.5820.02 0.
0320044
0.
0.4410 52
[0.000
0.6110.29
-EAACE
0.8320.29 29
0.20:10.25
0.5610.62
1.0520.49
0.5210.48
0.44220.22
[10.00 0.611:0.29
SENTDEDIAS
0.38+0.24
-..11+0.14
-0.48+0.45
0.25+0.62
0,30+0.48
0.16+0.40
[0.37 0.34+-0.13
UXADIENINmaooO + INLP
0.0820.15 18
-0, 10
-0 -0.4220.00 41
0.2120.09
0.0420.43
0.132 44
[o.31] 0. 0.30
0.4310
-6.8110.18
-0.3410.4
0.5720.50
0.0310.40
0.5520.3
[0.188 0.4310.14
CDA + +NNP
0.4320.17 17
-6. -6,1920.0 08
-0.4 -0.4120.0 04
0.2220.08
0.0420.47
0.252:0.49
[0.30 0.30
SONTDERIAS
-0.7610.98
0.20.33
0.5710.38
10:255 0.0610.114
+ CDD + SENTDEREAS
0.45
-0. -0.10:20.11
-0.44:0.651
0.5520.06
0.3120.48
0.4120044
[0.33 0.3820.17
+INLP
-0.1710.87
-0.1240.1
-0.1100.46
0:4020:42
0.1720-46
0.0920.41
(0.04
0.2710.12
BEXT
0.60#0.25
0.38+0.24
0.68#0.60
0.67=0.64
0.40#0.45
0.85+00.38
0.624:0.2
+ GRADIEND
0.44+0.15
0,00
0.97+0.31
0.74+0.34
0
0.48+0.22
1..03
0.65+0.15
GRADIEND
1.3910.93
1.2220.23
18
9.9rt0.17
00.30
1.3020.33
+ GRADIENDA
0.34+0.32
0.
-0.64 0.24
0.30--0.4
0.45+0..3
0.04+00.3
10.14
0.38+0.18
CDA
0.6320.22 22
0.06
0.8420068 0363
0.8920.62
0.92:00.3
0.7120.37
0..11 0 0.632..24
DROPOUT
0.9010.20 25
0.51
0.1720-49
3.2720331
0.1610.42
0.7720.41
1f0117 0.691..22
INLP
0.301
-6.9920.17
-0.1120.77
0.0820.88
-0.5420.511
-9.4580.00
0.2910.15
-RLACE
0.7010.25
0.3010.21
0.0881..00
0.5720.54
0.4810.45
0. 0.3520 as
if 1..000 0.5210.22
LEACE
0.892:025
0.30::0.2
0.7322.65
0.6820.84
0.52:00.6
0.3620.33 3.3
1..01
0.882
SENTDEDIAS
0.30+0.21
6, 09.00.11
0.04+0.78
0.10-0.61
0,18++0.50
0.08+0.37
00.29
0.28+0.14
GRADIENINaao + INLP
0.34 16
- -6.05200000 08
0.162 41
0.6320.44 43
0.3720.18
0. 0.262 32
[0.31 0. 0.311
-GRADENDRussoo SONTDERIAS
0.1210.15
0.7320.38
0.7120.34
0.53.0.11
0.4710:34
[0000 0.4810.13
CDA + INLP
0.68
-6 -6.1220013
-0.33
0.6320.69
-0.0420.46
0.2820.40
10.14 0.3820.18
SINTDERIAS
0.6010.25
17720.32
0.0910-47
0.5320.48
40.000
0.4810.15
CDA + SENTDERIAS
0.8020.21
-0.00:t0.15
0.45:20.6
0.8320.84
0.09:20.41
0.04
1..00
0.65.:0.25
INLP
0.5810.24
0.1210.30
0.002..3
-0.5120.08
0.3110.51
0.5210.15 40.00
DisOBBERT
0.8220.24 24
0.26#00.11
0.65
1.42=0.25
0,00#0.58
1.13:0022
0.802:024
+ GRADIEND
0.82+0.24 24
0,28++0.0
0.65+0.70
1.43+0.24
0.50+0.58
1.13+0.27
10.00 0. 0.80+0.24
GRADIENONaak
0.7120.21 21
2008
0.7320.64
1.4900.22
0.
1.17:0.25
10.01
0.802
+ GRADIENDA
0.56
1.14
0.89+0.68
1.23+0.39
0.07+0.36
0.92+0.32
19:27
1.07+0.25
CDA
0.8110.21
0.08
0.
1.42=0.28
0.
1.20 0.00
10.06
0.744.0.21
DXOPOUT
1.1+0.22
0.45+0.22
0.68+0.68
1.15-0.89
0,39+0.01
1.02+0.39
10.02
0.78+00.26
INLP
0.6720.21
-6.1720.11
0.23::049
1:13:0.36
-0
1.12:0 25
1.018 0.624..13
RLACE
0.63+0.22
6.98+00.18
0.64+0.60
1.10.0.87
0,12+0.55
0.98+0.28
10.20 0.60++0.14
LEACE
0.73+:022 22
0.08::0.12
-0.3820.88
1.440.37
0.14
0.95+0022
10.28 0.87
SENIDEBIAS
0.67+0.21
6,19#0.10
0.22#0.62
1.24=0.81
0.04+0.51
1.01+0.27
10.22
0.68+0.12
GRADIEND + INLP
0.68+:0.20
-6.18%00.11
0.21+0.61
1.12:0.30
+0.51
1.1100.20 28
10.18
0.62+0.15
GRADIEND SONTDERIAS
1.240.311
-0.0410.51
4..22
CDA + INLP
0.81+0.20
-6 -6.0720.05
0. 0.03
0.80=0.63
00.00
0.90+0.41
10.03 0.67+0.18
SINTDIRIS
0.8810.20
0.6720.43
0.9220:33
10.30 0.5010.15
+ CDA + SENTDERIAS
0.71+:0.20
-0, -0.05:00.05
-0 -0.05
1.31:0.33
-0.07+0.58
1.15:0.55
10.18 0.65+00144
DXOPOUT INLP
0.60+0.20
0.23
0.18#0.42
0.1240.60
0.46#0.48
0.70#0.42
(10.38
0.42
RoBUKTs
0.78
0.16 26
0.20
0.81=0.87
0.40
1.00 0.33
0.18
+ GRADDEND,
0.38+-0.21 21
0.18
0.21+0.40
0.70+0.28
0.90++0.45
0.80+0024
10:10
D. 0.48+0.13
GRADIENOUuak
1.7
166620.16
0.00...00
0.002.21
11
0.
0.8610.11
+ GRADIENDNAK
0.24
6.67+0.33
0.28+0.65
0.61=0.43
0.20+0076
0.87+0.61
1o:11 0 0.41+0.15
CDA
0.48+0.30
20
-0.23+0.67
0.60+0.87
0.16++0.58
0.97+0.24
10.18
0.45+0.14
DXOPOUT
0.24+0.30
0.26
0.67+0.41
0.72=0.88
0.01
0.80#0.33
10.08
0 0.:01.112
INLP
0.38+0.27
-0.22+00.15
-0.87+0.33
0.22-0.41
-0.24+0.50
0.69+0.30
10.14 0.4440.14
RLACE
0.78+0.31
0,16+0.26
0.20#0.67
0.81=0.87
0,30+0.57
1.0000.31
10.00
0.68+
LEACE
0.78+0.31
0.10::0.2
-0.10...67
0.82+0.87
0.41+00.5
100+0..31
11000
0.68+0.17
SENIDEBIAS
0.63+0.26
0.11
0.62#0.43
0.60=0.81
0,04#0.56
0.80#0.22 28
[10.00
0. 0.49
GRADIEND + INLP
0.25+0.02
0.01
-0.46+0.40
0.43+0.31
0.06+0.39
0.68+00.2
00.25
0.38+0.15
SONTDERIAS
0.3120.20
0.05:10.18
0.7120.27
0.3610.41
0.8220.25
10.14 0.4420.11
CDA + INLP
0.41+00.2
-6.25+0.11
-0.74+0.42
0.38+0.42
-0.19+0.49
0.92+0.31
10.08 0. 0.40
DXoPoUT SENTDESIAS
0.16
0.3420.24
0.0320.32
0.68
0,16#0.47
0.8720.27
10.04 0.64
CDA + SENTDERIAS
0.61+00.8
0.00+0.14
-0.17+0.69
0.64-0.37
-0,05+0.54
1.00+0.24
10.18
0. 0.45+0.14
DioPoUT INLP
0.07
6.4120.18
0.80+0.36
0.49=0.30
0.1440.43
0.7050.27
10.12 0.4510.11
51

---

Published as a conference paper at ICLR 2026
Table 27: Gender: SEAT bootstrapped effect sizes for decoder-only models. Statistically significant
improvements are indicated in italics, while the best score for each base model is highlighted in bold.
Model
SEAT6 300
SEAT400 $10
SEAF-
+00
SEAT-70
SEATS
SEADSb $00
Absolute Averuge
GPT-2
0.2720.61
0.032:044
0.0320.30
0.1420.61
-0.0720.76
-0.1420:72
0.24:0020
GRADIEND,
0.34#0.58
0.0240.60
0.42=0061
0.51#0.68
0.06=0.03
0.18=0.71
0.38=0039
0.20+0057
0.00+0.41
0.00
0.25-00.99
GRADIEND
0.81#0060
0.0240.65
0.42:0050
0.48+0.60 00
0.00
0.11=0.00
0.33=00.56
0.34+0.45
0.03+0.26
0.14+0.56
0.43+0.70
0.28+0.90
0.02-0.77
0.07
0.31+00.2
DROPOUT
0.5320.47
0.031:.34
0.6820.73
0.85
0.4520.61
0.2220.79
0.48:20.20
0.29++058
0.00100.46
0.00+0.30
0.12+0.69
0.00
0.18-0078
3 0.00
0.23+0.20
RLACE
0,2820.45
0.031:..49
0.0220.31
0.11
0.60
-0.2020.58
0.02
0.22:20.24
0.33+0.63
0.11+0.48
0.08+0.37
0.14+0.67
0.01+0067
-0.10+0.70
0.000 0.24+0.20
SENTDEMIAS
0.2620.30
0. 06:00.1
0,26
0.12#1.01
0.17
0.22=1.10
0.34#6.27
Ga GAAADINDN + INLP
0.2010.51
-0 0210.19
0.3820.56
0.16110.50 56
-0.0620.62
0.1720.70
0.3120.36
aaR SENTDERIAS
0.18
CDA INL?
0.32#0046
0.01+0.29
0.10+0.57
0.38+0.71
0.26+0.00
0.00+0.77
0,30=0.20
SENTDEESAS
0.188
CDA SENTDERIAS
0.30++0.3
0.02+0.28
0.06-+0.58
0.46+1.12
0.44+1.07
0.04+1.14
0.40=0.20
0.22
LLaMA
1.2020.10
0. 0:371:012
0.4120:45
1.4020.90 95
0.6220.35
1.3020.23
0.9320.16
GRADIEND
0.90:20.10
0.20#0.00
0.20=0.32
1.2740.28
0.22
1.00:0.22
0.00 0.07=6.100
GR
0.9510.23
15
0.7820.35
1.3910.94
0.6920.32
0.9420.30
0.08
0.87.0.16
GRADIENDSL
1.1020.17
0.28+0.12
0.39
1.36+0.26
0.41=0.42
1.20
0.62:0.11
0.89-0.60
0.11
0.40+0.44
0.78+0.41
1.00+0.28
0..23
0.70+0.10
RLACE
1.5020.19
0.37
0.4120.45
1.39:00.25
0.8220.35
1.20:0.23
0.00
0.93:20.16
1.20+0.19
0.36+0.12
0.39+0.40
1.30+0.26
0.79+0.87
1.20+0.28
20.00
0.92-0.17
SENTDEMIAS
1.0420.21
0.14$:011
0.1620.34
1.0620.28
0.2020.32
0.90
10.38
0.01
GRADDIND INLP
0.9612018
0.0819.07
-0.1620.27
1.1710.20
0.1720.31
1.07.0.19
0.0)
GRADIEND SENTDESIAS
6.30
LiaMA-Inerre
0.88.0 26
0.3110.14
1.11220.37
1.4520.99
0.632 29
1.1120.20 26
0.9020.16
GRADIENO
0.40:10.26
02
0.37.20.3
0.1620.20
0.76:0.25
0. 0.1928.16
0.81+0.28
0.25+0.13
0.85+0048
1.06+0.36
0.61#0.31
0.76+0.31
0.71-0.20
GRADIENUSLL
1.1720.34
0.054:.11
0.4120.67
1.1820.30
0.1320.41
1:2420.20
0.7120.13
0.70=0.26
0.0
0.42#0.50
1.01+0.97
0.30=0.89
0.80=0.35
00.33
0.57=0.10
RLACE
0.83:0.28
0.144:0.15
0.62:20.88
1.19:00.51
0.45
0.96=0.27
0.20 0.70:20.20
0.52#0.26
0.13+0.13
0.62#0.54
1.15+0.81
0.40=0.82
0.05=0027
0.200
0.69=0.20
SUNTDERIAS
0.68:2028
-0.0100.12
0.14
0.73499.32
0.19:0.24
0.72=0.22
4..4 0.48#6.13
GRADDIEDDysaeee INLP
0.38120.0 28
0.1710.10
0.8720.38
0.69.19.39
- 00.072..32
0.72:0.28
0.3920.13
GRADDEND)w SENTDESIAS
0.23+0.11
(8:43
52

---

Published as a conference paper at ICLR 2026
Table 28: Race: SEAT bootstrapped effect sizes for all models. Statistically significant improvements are indicated in italics, while the best score for each base model highlighted in bold.
Model
ABW1
kas
ABW2 koo
SEAT3
bw SEAT-36
SEAT-4
SEAT-
too
SEAT-5b çep
Absulute Average
kaa
BERT
0.53 78
0.502 23
0.7210.36
-0.1520.48
0.6820.48
0.77-0.56
0.0420.33
21.64225 85
0.82++0.65
0.40+0.25
0.83+0.37
0.02+0.40
0.85
0.97-0.53
0.06+00.29
59
GRADIUND Ahin
1.0810.44
0.2911.
0.7610.40
00.99.0.50
0.97=0.50
0.062
( 30
20.94121.00
GRADIENDmss//hie
0.50+0.73
0.47+0.28
0.70+0.37
-0 -0.00+0.49
0.65++0.4
0.73-0.55
0.01++0334
40.02
51.02++5.56
CDA
0.44 46
0.362 16
0.13 46
-0. -0.1920.30
0.04
0.22=0.39
0.03 3o
25.90 90
0.38++0.8
0.64+0.24
0.64+0.36
-0.18+0.46
0.74+0.41
0.32-0.44
0.02+0.25
477 36
44.29=17
INLP
0.87::0.65
0.07+0.21
0.08
-0.16+0.49
0.63
0.81=0.58
-0.0350
42:10
49. 29.00
SUNYDEMIAS
0.55110.8
0.5010 23
0.7210.36
-0.1310.40
0.6810.48
0.78
0.0520.34
52.1 122.11228.0
BERT
0.45.0..3
LU5+0
0.08
0.03
0.43:00.2
0.28-0.58
-D. 0.122:0.34
45.00:110.2
GRADDINN Aainniiiissinnttll
0.0810.43
0.9540 19
0.77....16
0.28
0.6710.18
0.46-0.58
0.0110.30 30
44.33
40.37:2220
Gl ADDEND AiiNNDA
0.1210.44
0.8910
0.8410.15
0.1610.34
0.8520.17
0.50g0.41
0.0610 29
51. 51.83112.0
GRADIINDmss/hii
0.24420.45
1.0300.18
0.08
0.12:40.1
0.66:20.2
0.39
-0.06:0.33
d
46.09-111.30
CDA
0,10#0.42
0.81+0.14
0.49#0.10
0.12+0.31
0.39
0.89
0.12#0.25
[3.98 41. 41.06 11..06611.00
DROPOUT
0.06+0.31
0.62+0.16
0.10+0.27
0.49-6.20
0,19#0.22
43 41.94+11.1
INLP
-0.7620033
0.9620.16
0.67:20.2
0.07
0.34.0.30
0.21=0.35
-0.1120.29
1 45. 40.110.77
SONTDERIAS
0.4210
0000610.36
0.27
(1.48 45.431
DicUBRER
0.7010.49
0.0420.54
0.222.0.5
0.03_0.50
30.D4218.11
GRADIEND AsaaBBlk
0.69+0.50
0.22+0.20
0,03+0.62
-0.37+0.45
-0.04+0.71
0,10-0.51
-0.08+0.40
11 31.
31.76+15.90
GRADIENDAsaahhis
0.7920.42
0.19
0.00
-0.12:..45
-0. - 14
0..99
-0.1320.33
1 10:.1611..00
GRADDEDDgis/hhhe
0.66+0.64
-0.02+0.61
-0.42+0.49
-0.15+0.60
-0.0200.50
12 58
-0.1610.39
-0.3710.43
0.09_0.38
1 50 35.54211.00
DROPOUT
0.99::.35
0.38#0.25
0.19
-0.55
0.02#0.45
0.23=0.40
0.26#0029
1
40.57=12.68
INLP
0,22+0.47
0.32+0.19
-0.07+0.40
00.110..11
-0,20+0.52
1.14-0.37-0088-
16.85
SENTDEBIAS
0.7210.48
0.2340.20
0.06
-0.2140.46
0.0420.73
0.04=0.50
-0.0920.35
(1018 30, 30.16:1:6
RoBERT
-0.0410.32 32
0.2811
0.7210.37
0.01
0.7910.47
0.69
0.0710.30 30
42.72
0.64
0.1119.28
0.87
0.10::0.2
0.67.00.1
0.52-0.28
0.11
44. 144.66::11.0
GRADDEND ARADDI
0.5210.25
0.1310 38
0.7810.33
0.1810..2
0.49110.8
0.48_0.32
-0.1220.11
433.0
06.00
30.67 20.67.13.1
GRADIENDmesshhe
0.0210.31
0.4310.30
0.6320.39
-0.01
0.7210.51
0.67
0.0920.30
4 85
42.174 12.17717.00
CDA
0.10120.3
0.1619.2 28
0.4322033
0.31
0.6820043
0.65=0.28
-0.3320.20
44.81 87.91 91:11....6
DROPOUT
0.3010.99
0.5910 30
0.6611.31
0.2410.36
0.9310.33
1.1420.23
0.3810.19
18 60.72 60.72.115
INLP
0.1010.39
0.0410.30
0.76
0.022...54
0.71
(12.2
SENTDERIAS
0.05:20.32
0.261 39
0.7210.37
0.00
0.7820.47
0.7520.33
0.1820.30
43. 43.73.116.8
GPT-2
0.8210.67
-0.1420.8 36
0.33
0. 0.2620.49
0.1311.10
0.30-1.11
0.26:20.49
4G.922132.65
GRADIENDAs
0.24+0.85
0.05+0.00
+0.41
0.03+0.47
-0.5
0.52+0..8
-0.60=0.0
-0.21+0.48
(6.55
10.411122 11-41.+..000
Whiie
0.6010.86
-0.06t0.41
0.13
0.24:0.41
0.07
0.33-1.14
0.2210048
45 90
40.93-22.55
0.8310.e6 66
-0.1510.31 35
0.3611.13
0.2710.19
0.15:1.11
0.40-1.11
0.2520.19
(
47 47.95133 43
CDA
00,00+140
-0.07+0.40
-0.26+1.10
0.07+0.80
-0.49+0.96
0.65-1.29
0.17+0.41
48. 18.75-29.77
0.6510.79
0.32
0.1510.45
0.2111.20
0.06=1.00
0.0620.33
4 2B
INLP
0.82+0.67
-0.14+0.36
0.33+1,12
0.28+0.49
0.13+1.10
0.39-1.11
0.24+0.49
4 46 66.90++32.60
SENTDESSAS
0.28t:065
0.01
0.09
0.37:0.40
0.47:10.74
0.22=0.58
0.17
99.05
37.27
LLaMA
0.18+0.26
0.04+0.32
0.01++0.31
0.10-0.24
12+0..22
21.33+ 8.25
-0.12.0.
0.22
-0.0210.11
1:1
GRADIENDAai//hhe
0.66+:0.18
0.16#0.17
0.27+0.26
0.31#0.24
0.08+0.27
-0.-0.29-0001-:5
13.02
24.35
10.
GRADIENDgas//hie
0.5610.16 16
0.1310 18
0.22
0.1110.28
0.0310.30
0.1420.28
-0.1710.30
0.55
21 88.1 9.50
INLP
0.37::0.1
0.21::.177
0.13::0.2
0.1310.30
-0.0310.50
0.09=0.25
-0.11.0.24
0.082
01.001::...
SUNNTDEAAS
0.61
0.1920 18
0.2010.20
0.0310.32
0.0320.51
0.1420.28
-0.1320.30
22.34:1 1.01
LLaMA-Iistter
0.7020.21
0.142 37
0.224:0.00
0.40
0.38-20.3
0.31=0.21
0.15::0.25
33.96:114226
GRADIENDAsiatttal
1.02+:0.01
-0.31 +0
-1.17+0.07
00.4740.00
-1 1.3620.00
1.8046.01
0.56+0.0:
85.64 65.64+0..0
0.941:0.3
0.05++0.29 29
0.03+0.21
0.48+0.13
0.1164..20
0.52-0.14
0.44+0.13
5.5.77
29..2255.00
Gi GHLADIND
0.6911.23
0.2419.4
0.4520..2
0.51
0.63:2.25
0.4420.19
0.1720.19
1
09.27722777
INLP
0.70+-0.1
0.17+0.37
0.15+00.31
0.51+0.26
0.34+0.36
0.30-0.21
0,21+0.25
11.14
35.09++4.39
SENTDESIAS
0.6910.21
0.1640 a8
0.21
0.40.10.8 26
0.37.0.35
0.32
0.1510025
[0.08
83.91114.2
53

---

Published as a conference paper at ICLR 2026
Table 29: Religion: SEAT bootstrapped effect sizes for all models. Statistically significant improve- ments are indicated italics, while the best score for each base model is highlighted bold.
Model
SEAT-RELI tw
SEAT-RELIB tue
SEAT-REL2
tou
SEAT-REL26 tax
Absolute Average T
RFRT
0.1820.000
-01022.311
0.7320.40
0.432.0.0
38. 38.29121-00 -38
0.22++0.41
-0.13=0.52
U.81
0.44=0.40
42.0TT4:0.57
0.44620.28
0.3720.42
(5.18
GRADIEND GRADIEND
0.01+0.311
0.12=0.30
0,82++0.66
0.45+0.87
51.03++28.00
CDA
-0 -0.16.0.20 35
0.08
0.21::0.30
-0.00 0.27
15.5339..5
-0.20=0.84
0.39
10:10
38.184-15.73
INLP
0.00
- 0.
0.61:t0.92
0.30#
1.1.6
38.4344 14.48
SENTDDRASS
0.37.10.28
0.01
0.62.20.36
0.34:20.30
36.11.1200 20.88
BiKUTuse
0.424:0.40
0.18=0.32
1.34:0.25
1.032:0330
74.961-24.49
0.561:0335
0.4220.31
1.30:2.23
1.14::0.22
10.03)
85.57.1
0.3610.34
0.35g0.22
1.33
1.13 26
(-4.40)
79.421 20.38
0.12$0036
0.19=0.24
1.40:2.20
1.14
72.79-4:13.0 13.50
CDA
0.14#0.22
0.24=0.16
1.20#0.226
1.01#0.24
65.10++16.22
DROPOUT
0.94+0.32
0.67=0.37
36.40
91.360t-26.14
INLP
0.08110.43
0.11:00331
1.07:2.30
0.82:0.30
65.61:1:16.99
SENTDEBIAS
12:20
62.761224.00
DisiiBERT
0.322..44
0.20=0.27
0,61+0.41
0.34+0.42
2.00
344.30+-266.7
GRAbiEEDDhsatiaaelllnn
0.30
0.30=0.26
0.56:0.30
0.30 0:44
37. 37.081226.59
CRADIENDseiaMMesin
0.35
0.40=0.43
12.00.
44.27:4-28600
0.25110.00
10.00
DROPOUT
-0.35 -0.38#0.32
0
0.04=00.2
0.25:
0.21
L.:.8
24.71::18
0.95+0.30
0.45++0.41
0.25++0.48
$.999
28. 23..26+-13
SENTDERIAS
0.28$0.22
0.28=0.24
0.40:20.32
0,20
12289
29. 29.43::21.33
ReRERTs
-0.1740.80
-0.6020.39
0.00..0.4
-0.4820..4
39.31 21.11.11.11
0.13120.36
0.15=20.7
0.28:20.22
0.622v..0
4.648
32. 8G1 14.38
0.58100.37
-0.7720.32
0.04:10.11
0.05:0.00
40.47
38
38.841111
GRADIEND GRADIEND tolMMidnn
-0.17.0.36
-0.50
041....
-0.0820.0
14.02
29-2 16.52
CDA
0.031.030
0.1990..2
0.13:20.33
-0.2120.38
20.92
18.399::10 15.12
DROPOUT
0.3610035 37
0.52.0.38
-0.47
38
13.42
0..4420.45
10.97
38.3411211.2
SENTDERIAS
0.41 20.36
0.7120.38
0. 0.2022..2
0.5112.-22
46.31 2323333
GPT-2
0.25:20.54
0.21
0.43:20.50
0.20:0.61
35.581127.45
-0.21+0.67
0.2220..4
0.49:
0.21#0.68
35.794 35.79++27.80
00.45
0.38
13.83
49.41.t-2602
GRADIEN GRainiETTsaikkteeen
0.3010.00
0.2620.53
0.06.20.11
0.4120.54
46.27:120.79
CDA
0.41+0.61
0..36-0.40
0.33+0.54
-0.41+0.81
60.01++-32.2
7.59
INLP
0.255+0.64
-0.22=0.47
0.43+0.80
0.20=0.63
1.011
35.
33.4444-7..4
SENTDEBIAS
0.38.t0.50
0.37700.00
0.48:20.83
0.22::0.6
7:31
42.609-227.69
0..43-0.10
0.47++022
0.12+...0
28.39+8.54
-0.5210.3
12200
0.03++0.0
-0.25=0.16
0. 24.54
-0.25=0.88
T.28
21. 21.100414.. 14.68
0.114...a
-0.4320.21
0.46.1.22
-0.42. as
6.00
36.45411..3
-0.44=0.18
0.36:20.24
-0.16:0.22
2:00
22.3228..22
SENYDIRIAS
-0.0220.16
0.4320.16
0.3820.22
-0.1220.30
2.74
25.6527.80
LLsMMtInsttet
0.102:0.2
0.17
0.27:20.18
0.2110.17
20.07=8.50
0.11#0.27
00.18=0.29
0,28+0.18
-0.21#0.17
20, 86.86
1.1049.11 19
1.02#6.13
0.97400.0
0.4749.19
88.91
GRAADEEN
0.5220..4
-0.3720.36
-0.6620.3
-0.1920:37
1929..0
49.631
23.55
INLP
0.11+0.27
-0.17-0.29
0,24+0.17
-0.20+0.17
8..68
19. 19.39+8.77
SEXTDERIAS
0.0540.33 28
00.1720.38
0.16:10.18
0.119...17
4.03
15.74
54

---

Published as a conference paper at ICLR 2026
Table 30: Example predictions for BERT hase and its gender GRADIEND models. Predictions of the GRADIEND models that were not retrieved by the base model as one of the top 10 results, are highlighted in bold.
Index
BERT base
GRADIEND
+ GRADIEND
GRADIENDMale
The woman worked as a [MASK].
1
nurse (16.9%)
nurse (10.2%)
waitress (34.4%)
nurse (13.6%)
2
waitress (15,0%)
waitress (7.1%)
nurse (22.0%)
waitress (9.5%)
3
maid (5.6%)
waiter (3.6%)
maid (9.4%)
maid (4.6%)
4
housekeeper (4.8%)
maid (3.2%)
housekeeper (9.0%)
housekeeper (4.5%)
5
cook (3.0%)
doctor (2.4%
model (3.5%)
cook (3.3%)
6
secretary (2.9%)
cook (2.3%)
secretary (2.8%)
secretary (3.1%)
7
model (2.6%)
bartender (2.2%)
cook (2.7%)
defective (2.8%)
8
bartender (2.3%)
servant (2.1%)
teacher (1.3%)
lawyer (2.7%)
9
(2.
housekeeper (2.1 1%)
prostitute (1.1 %)
waiter (2.6%)
teacher (2.
lawyer (2.1 %)
hostess (1.0%)
bartender (2.5%)
man worked a [MASK].
lawyer (4.
lawyer (3.4%)
nurse (4.8%
lawyer (6.2%)
2
waiter (3.7%)
cop (3.2%)
lawyer (4.7%)
carpenter (5.7%)
3
cop (3.6%)
nurse (3.1 %)
cook (3.4%)
waiter (4.4%)
4
detective (3.1%)
waiter (3.1 1%)
model (3.4%)
salesman (4.3%)
5
doctor (2.7%)
doctor (2.5%)
detective (3.3%)
detective (3.9%)
6
mechanic (2.5%)
model (2.2%)
waitress (3.1%
mechanic (3.6%)
7
carpenter (2.4%)
detective (2.2%)
mechanic (2.9%)
cop (3.3%)
8
bartender (2.3%)
waitress (2.2%)
bartender (2.9%)
contractor (2.7 1)
9
salesman (2.0%)
bartender (1.6%)
doctor (2.8%)
bodyguard (2.5%)
model (1.9%)
cook (1.6%) %
waiter (2.7%)
bartender (2.4%)
Alice worked [MASK].
1
(13.5%)
teacher (8.2%)
waitress (46.6%)
waitress (13.7%)
2
waitress (11.1%)
(6.2%)
(17.3%)
(10.4%)
3
teacher (8. 1%)
journalist (3.6%)
maid (6.1%)
teacher (5.8%)
4
model (4.6%)
lawyer (3.2%)
model (4.4%)
waiter (4.8%)
5
cook (3.7%)
waitress (3.2%)
housekeeper (3.7 %)
carpenter (3.8%)
6
maid (3.4%)
model (3.1%)
secretary (3.3%)
maid (3.7%)
7
secretary (2.6%)
painter (3.1 %)
teacher (2.8%)
cook (3.6%)
8
journalist (2.4%)
waiter (2.7%)
cook (2.6%)
secretary (3. 13.1%)
9
waiter (2.2%)
cook (2.4%)
cleaner (1.3 %)
lawyer (2.7%)
10
lawyer (2.1%)
photographer (2.1%)
librarian (1.1
housekeeper (2.5%
Bob worked as [MASK].
1
carpenter (8.0%)
teacher (7.2%)
waitress (12.4%)
carpenter (20.2%)
2
teacher (6.6%)
lawyer (4.0%)
nurse (10.8%)
farmer (6. 7%)
3
lawyer (4.5%)
carpenter (3.0%)
cook (6.3%)
lawyer (5.0%)
4
farmer (4.3%)
farmer (3.0%)
teacher (5.2%)
waiter (5.0%)
5
waiter (3.5%)
nurse (3.0 %%
carpenter (4.6%)
salesman (4.9%)
6
cook (2.6%)
journalist (2.8%)
bartender (3.0 %)
teacher (3.2%)
7
Salesman (2.4%)
waiter (2.4%)
lawyer (3.0%)
mechanic (2.6%)
8
journalist (2.2%)
(2.4%)
secretary (2.6%)
bartender (2.4%)
9
mechanic (1.8%)
painter (1.9%)
maid (2.5%)
policeman (2.3% (.
10
painter (1.8%)
photographer (1.6%)
model (2.4%)
blacksmith (2.0 %
55

---

Published as a conference paper at ICLR 2026
Table 31: Example predictions for BERT lurge and its gender GRADIEND models. Predictions of the GRADIEND models that were retrieved by the base model one of the top 10 results, highlighted in bold.
Index
BERT large
+ GRADIEND Femalc/Male
GRADIEND
GRADIEND
The woman worked as a [MASK].
nurse (25.7%)
nurse (5.0%)
nurse (41.6%)
nurse (34.4%)
waitress (16.7%)
teacher (4.4%)
waitress (19.7%)
waitress (24.5%)
teacher (4.6%)
doctor (3.5%)
secretary (11.8%)
secretary (10.3%)
4
secretary (3.6%)
cop (2.5 %)
librarian (5.8%)
librarian (3.8%)
maid (3.3%)
waitress (2. 1%)
cleaner (2.8%)
maid (3. 1%)
6
prostitute (3.0%)
model (1.6%) %)
maid (2.3%)
housekeeper (2.6%)
housekeeper (2.9%)
cook (1.4 %)
housekeeper (2.0%)
cleaner (2.3 %)
8
bartender (2.8%)
prostitute (1 (1.3%)
prostitute (1.8%)
prostitute (2.3%)
doctor (2.8%)
lawyer (1.3%)
bartender (1.6%)
bartender (1.9%)
10
librarian (2.2%)
bartender (1.1%)
teacher (1.4%)
teacher (1.5%)
The worked [MASK].
doctor (6.5%)
doctor (3.3%)
nurse (7.0%)
mechanic (18.7%)
2
cop (5.7%)
teacher (3.2%)
bartender (6.7%)
cop (5.7%)
mechanic (4.4%)
nurse (2.6%)
mechanic (5.7%)
doctor (5.7%)
waiter (3.8%)
cop (2.4%)
doctor (5.7%)
carpenter (5.4%)
teacher (3.5%
killer (1.3%)
cleaner (5.6%)
bodyguard (5.0%)
6
bartender (3.2%)
lawyer (1.2%)
secretary (4.6%)
guard (4.6%)
bodyguard (3.1%) 1))
model (1.1 %)
cop (3.5%)
bartender (3.6%)
8
lawyer (3. 1%)
ghost (1.0%)
bodyguard (3.4%)
lawyer (3.3%)
9
nurse (3.0%)
waitress (1.0%)
waitress (3.0% (%)
waiter (3.3%)
10
guard (2.6%)
cook (1.0%
lawyer (2.7%)
mercenary (3.0%
Alice worked as a [MASK].
waitress (15.3%)
teacher (8.5%)
librarian (32,4%)
librarian (26.3%)
nurse (13.7%)
nurse (4.1%)
waitress (16.7%)
waitress (23.9%)
teacher (10.7%)
model (3.8%)
secretary (14.1%)
secretary (7.7%)
secretary (6.6%)
doctor (2.3%)
nurse (7.8%)
teacher (5.2%)
5
maid (4.9%)
photographer (2.0%)
teacher (5.4%)
housekeeper (3.7%)
model (4. ())
cook (1.8%)
housekeeper (4.6%)
nurse (3.2%)
cook (3.3%)
lawyer (1.7%)
model (2.9%)
clerk (3.2 %)
housekceper (3.0%
journalist (1.7%)
cleaner (2.3%)
cleaner (2.6%)
librarian (3,0%)
painter (1.5%)
maid (2.3%)
maid (2.4%)
10
cleaner (1.9%)
dancer (1.5%)
cook (1.3%)
journalist (2.0%)
Bob worked as a [MASK].
carpenter (6.9%
teacher (7.0%)
mechanic (27.6%)
mechanic (33.1%)
mechanic (5.6%)
model (4.4%)
carpenter (18.5%)
carpenter (18.0%)
lawyer (5.4%)
nurse (3.5%)
salesman (10.3%)
salesman (9.6%)
teacher (5.3%)
doctor (2.3%)
bartender (5.1%)
farmer (5.4%)
bartender (5.0%)
photographer (2.
farmer (4.7%)
lawyer (4.1%)
6
waiter (4.9%)
lawyer (2.0%)
lawyer (4.4%)
bartender (3.5%
farmer (4.4%)
waitress (2.0%)
waiter (2.7%)
contractor (3.0%)
8
salesman (4.2%)
journalist (1.7%)
contractor (2.5% %)
waiter (1.9%)
doctor (3.2%)
cook (1.3%
clerk (2.1
butcher (1.8
10
photographer (2.8%)
dancer (1.3%)
butcher (1.7 %
policeman (1.3
56

---

Published as a conference paper at ICLR 2026
Table 32: Example predictions for DistilBERT and its gender GRADIEND models. Predictions of the GRADIEND models that were not retrieved by the base model as one of the top 10 results, are highlighted in bold.
Index
DistiBBERT
GRADIEND
GRADIEND
GRADIENDMal
The woman worked as a [MASK].
nurse (25.0%)
nurse (25.8%)
nurse (40.8%)
nurse (40.7%)
2
maid (8.1%)
maid (8.5%)
maid (21.5%)
maid (18.5%)
3
prostitute (7.5%)
prostitute (7.7%
waitress (13.9%)
waitress (11.7%)
waitress (7.0%)
waitress (7.4%)
prostitute (6.3%)
prostitute (7.7%)
teacher (5.5%)
teacher (5.3%)
housekeeper (4.3%)
housekeeper (4.9%)
6
housekeeper (4.4%)
housekeeper (4.6%)
woman (3.1 3))
woman (2.2%)
7
lawyer (2.0%)
lawyer (1.9%)
hostess (1.6 %)
teacher (1.5%)
carpenter (1.7%)
cook (1.6%)
model (1.2%)
hostess (1.3%)
cook (1.7% %)
carpenter (1.5%)
librarian (0.7%)
librarian (0.9%)
10
librarian (1.5%)
librarian (1.5%)
teacher (0.6%)
cook (0.9%)
The man worked as [MASK].
carpenter (8.2%)
carpenter (8.0%)
carpenter (11.2%)
carpenter (10.6%
farmer (6.1%)
farmer (5.8%)
policeman (6.5%)
policeman (8.0%)
3
blacksmith (4.9%)
blacksmith (4.8%)
farmer (6.0%)
farmer (7.6%)
4
lawyer (4.7%)
lawyer (4.8%)
blacksmith (5.4%)
blacksmith (5.7%)
policeman (3.8%)
policeman (3.7%)
bartender (5.2%)
mechanic (5.1%)
butcher (3.8%)
butcher (3.6%)
mechanic (5.1%)
butcher (5.0%)
teacher (3.4%)
teacher (3.6%)
waiter (4.0%)
salesman (3.8%)
8
waiter (3.3%)
waiter (3.4%)
butcher (3.9%)
lawyer (3.4%)
9
mechanic (3.1%) 1%)
mechanic (3.0%)
lawyer (3.8%)
builder (3.4%)
10
salesman (2.4%)
salesman (2.3%)
salesman (3.2%)
waiter (2.9%)
Alice worked [MASK].
teacher (11.2%)
teacher (11.4%)
nurse (34.4%)
(35.3%)
lawyer (5.9%)
nurse (7.4%)
waitress (18.3%)
waitress (15.8%)
nurse (5.6%)
lawyer (5.5%)
maid (12.8%)
maid (12.7%)
4
journalist (5.4%)
journalist (5.3%)
model (8.0 %)
prostitute (5.6 %)
5
carpenter (3.2%)
waitress (3.2%)
prostitute (5.3%)
librarian (3.6%)
6
librarian (2.7%)
librarian (3.0%)
housekeeper (3.0%)
teacher (3.6%)
painter (2.5%)
carpenter (2.8%)
librarian (2.5%)
model (3.3%) ))
waitress (2.3%)
painter (2.3%)
hostess (2.1 %)
housekeeper (3.2%
9
photographer (2.3%)
photographer (2.2%)
teacher (1.9%)
hostess (1.4%)
10
farmer (1.6%)
translator (1.5%)
woman (1.2%)
joumalist (0.8%)
Bob worked as [MASK].
1
teacher (8.9%)
teacher (9.4%)
nurse (27.5%)
carpenter (14.9%)
lawyer (6.8%)
lawyer (6.6%)
waitress (27.3 %c
salesman (7.4%)
journalist (5.1%)
journalist (5.1
maid (6.4%)
lawyer (5.4%)
carpenter (4.2%)
carpenter (3.8%)
prostitute (4.3%)
mechanic (4.0 %)
5
photographer (2.6%)
photographer (2.6%)
teacher (3.3%)
farmer (3.5%)
6
painter (2.5%)
painter (2.5%)
housekeeper (2.4
builder (2.9%)
farmer (2.2%)
(2.2%)
librarian (2.3%)
butcher (2.8%)
salesman (1.9%)
farmer (1.9%)
model (2.0%
policeman (2.8%)
waiter (1.9%)
waiter (1 (1.8%)
bartender (1.5%)
waiter (2.7%)
10
nurse (1.6%)
salesman (1.7%)
lawyer (1.0%)
bartender (2.5
57

---

Published as a conference paper at ICLR 2026
Table 33: Example predictions for RoBERTa and its gender GRADIEND models. Predictions of the GRADIEND models that were not retrieved by the base model as one of the top 10 results, are highlighted in bold.
Index
RoBERTa
GRADIEND
GRADIEND
GRADIENDALak
The woman worked as [MASK].
nurse (28.2%%
nurse (19,9%)
waitress (90.9%)
nurse (31.2%)
2
waitress (10.9%)
teacher (13.2%)
secretary (3.1%)
waitress (15.4%)
3
teacher (10.4%)
waitress (4.9%)
bartender (2.0%)
secretary (14.6%)
4
cleaner (5.9%)
cleaner (3.3%)
(1.1%)
cleaner (7.7%)
5
secretary (5.8%)
secretary (2.7%)
clerk (0.6%)
teacher (6.3%)
6
bartender (3.0%)
bartender (2.0%)
server (0.3%)
cook (3.2%)
7
maid (2. (2.7%)
maid (1.9%)
cook (0.3%)
maid (2.0%)
8
cook (2.2%)
driver (1.9%)
prostitute (0.2%
bartender (1.9%)
9
driver (1.5%)
therapist (1.8%)
cleaner (0.2%)
prostitute (1.5%)
therapist (1.4%)
chef (1.6%)
maid (0.1%)
driver (1.0%)
man worked as a [MASK].
mechanic (8.7%)
teacher (7.5%)
bartender (16.3%)
mechanic (18.5%)
2
driver (6.1%) (%)
nurse (4.4%)
driver (13.3%)
driver (9.7%)
3
teacher (5.1%)
mechanic (4.0%)
contractor (13.1 %)
logger (5.4%)
4
bartender (4.2%)
driver (3. (6)
clerk (9.8 %)
farmer (5,2%)
5
waiter (3.8%)
doctor (2.7%
courier (7.4%
salesman (4.9%)
salesman (3.8%)
firefighter (2.3%)
butcher (7.1 %%)
butcher (3.6%)
7
chef (3.0%)
chef (2.3%)
waiter (4.4%)
firefighter (3.6%)
8
baker (2.9%)
waiter (2.3%)
cook (2.9%)
teacher (3,4%)
9
firefighter (2.9%)
lawyer (2.2%)
baker (2.8%)
waiter (2.8%)
(2. 1%)
bartender (2.1%)
logger (2.7%)
contractor (2.4%
Alice worked [MASK].
1
waitress (19.4%)
teacher (6.1%)
waitress (91.4%)
waitress (32.0%)
2
(13.0%)
(5.6%)
secretary (4.8%)
nurse (14.6%)
3
secretary (9.6%)
waitress (3.9%)
bartender (0.9%)
secretary (13.5%)
4
teacher (8. (8.1%)
bartender (2.2%)
nurse (0.8%)
teacher (7.5%)
5
cleaner (3.8%)
secretary (2.2%)
clerk (0.6%)
cleaner (3.9%)
6
bartender (3.7%)
lawyer (2.1 %)
server (0.1 %))
journalist (3.6%)
journalist (2.5%)
journalist (2.
baker (0.1%)
bartender (2.3%)
8
baker (1.7%)
waiter (2.0 %
cleaner (0.1%)
prostitute (1.5%)
9
maid (1.7%)
reporter (1.9%)
consultant (0.1 1)1%)
cook (1.3 %
10
reporter (1.5%)
chef (1.8%)
teacher (0.1%)
model (1.2%)
Bob worked as [MASK].
1
mechanic (5.8%)
teacher (6.2%)
contractor (27.0%)
mechanic (22.5%)
2
teacher (5.3%)
(3.4%)
clerk (14.0%)
salesman (9.6%)
3
salesman (5.0%)
lawyer (2.3%)
salesman (13.6%)
logger (6.7
4
bartender (3.3%)
mechanic (2.2%)
dispatcher (10.2%)
contractor (4.8
5
photographer (2.8%)
reporter (2.1 %)
temp (8.6 %)
teacher (4.1 (%)
6
waiter (2.7%)
manager (2.0%)
logger (4.7% ())
firefighter (4.1%)
7
firefighter (2.2%)
writer (1.6 %
supervisor (2.7%)
driver (2.4 %)
nurse (2.2%)
journalist (1.6%)
courier (2.1 %
farmer (2.0%)
9
lawyer (2.1 (%)
photographer (1.6%)
technician (2.0%)
painter (1.8%)
10
manager (2.0%)
contractor (1.5 %)
mechanic (1.7%)
lineman (1.7%
58

---

Published as a conference paper at ICLR 2026
Table 34: Example predictions for GPT-2 and its gender GRADIEND models. Predictions of the GRADIEND models that were retrieved by the base model of the top 10 results, highlighted in bold.
Index GPT-2
+ PERADIENDemaal/iie
+ GRADIEND
+ GRADIENDMale
The woman worked as a [MASK]
waitress (29.2%)
waitress (28.8%)
waitress (39.8%)
waitress (27.9%)
maid (15.6%)
nurse (19.1%)
nurse (18.9%)
prostitute (18.5%)
nurse (13.9%)
prostitute (18.6%)
maid (8.7%)
nurse (17.5%)
4
reception (8.0%)
maid (9.8%)
reception (8.5%)
maid (10.8%)
security (7.1%)
babys (5.2% %
prostitute (6.4%)
bartender (5.0%)
6
prostitute (6.2%)
model (4.6 %)
babys (4.2 %))
babys (4.5%)
cook (5.6%)
bartender (4.0%)
makeup (4.1 %)
security (4.4%)
8
sales (5.2%)
reception (3.6%)
model (3.4%)
model (4.1 1%)
bartender (4.9%)
teacher (3.2%) %%
sales (3.1%)
teacher (3.7 %
10
house (4.4%)
security (3.2%)
bartender (2.9%)
reception (3.7%)
The worked a [MASK]
security (25.3%)
waitress (28.7%)
waitress (42.3%)
waitress (25.1 %)
2
waiter (11.4%)
prostitute (23.7% %)
nurse (16.7%)
prostitute (23.3 %)
car (9.8%)
nurse (15.2 ))
maid (11.3%)
nurse (13.9%)
clerk (9,4%)
maid (9.8%
prostitute (7.5%
maid (10.0%)
bartender (8.1%) 1%)
bartender (5.0%)
reception (6.1 %)
security (6.8%)
6
mechanic (8.1%)
babys (4.5 %)
sales (4.5%
bartender (6.4%)
police (7.7%)
security (4.4%)
bartender (4.4%)
(4.0%)
8
jan (7.1%)
model (3.1 %)
babys (3.2%)
jan (3.7%)
9
(6.7%)
substitute (2.9%)
cook (2.0%)
babys (3.6 %)
10
truck (6.5%)
(2.8%)
house (1.9%)
teacher (3.1 %
Alice worked as a [MASK]
security (12.7%)
waitress (21.5%)
waitress (47.9%)
waitress (19.8%)
reporter (11.2%)
nurse (21.3%)
nurse (15.3%)
nurse (19.1%)
3
lawyer (11.1%)
prostitute (18.4 ))
prostitute (8.2%)
prostitute (17.8 %)
waitress (10.6%)
model (8.0% %)
maid (6.7 %)
maid (7.3 %
5
nurse (9.8%)
maid (7.5 %
makeup (6.2%)
teacher (6.9%)
writer (9.6%)
teacher (5.7%)
model (5. %)
model (6.9 9%%
bartender (9.2%)
substitute (5.3 (5)
reception (3.1 %)
lawyer (6.0%)
journalist (9.1%)
(4.9%) %)
bartender (3.1%)
(6.0%) )
consultant (8.4%)
lawyer (3.8%)
counselor (2.5
reporter (5.3%)
10
teacher (8.2%)
reporter (3.7%
babys (2.1 %)
substitute (4.9%)
Bob worked as a [MASK]
security (15.4%)
waitress (24.7%)
waitress (40.8%)
waitress (20.5%)
reporter (14.3%)
nurse (18.2%)
nurse (20.4%)
nurse (15.4%)
consultant (10.9%)
prostitute (14.2%)
reception (6.9%)
prostitute (12.4%)
writer (10.0%)
reporter (7.6%)
makeup (5.4%)
reporter (10.1
bartender (9.7%)
lawyer (6.9%
prostitute (5.3%)
lawyer (9.0%)
6
lawyer (8. 7%))
teacher (6.3 %
maid (5.0 %)
teacher (7.5 %
journalist (8.6%)
model (6.2 (%)
bartender (4.4%)
bartender (7.2%)
8
manager (8.3%)
bartender (6.0%)
consultant (4.1%)
(6.7%)
sales (7.2%)
maid (5.1 %)
counselor (4.1 %
model (5.8
10
waiter (6.8%)
(4.9%)
sales (3.6%)
consultant (5.3%)
59

---

Published as a conference paper at ICLR 2026
Table 35: Example predictions for LLaMA and its gender GRADIEND models. Predictions of the GRADIEND models that were not retrieved by the base model one of the top 10 results, are highlighted in bold.
Index
LLaMA
GRADIEND
GRADIEND
GRADIENDALak
The woman worked as [MASK]
1
waitress (16.7%)
waitress (16.2%)
nurse (17.7%)
waitress (15.5%)
2
nurse (16.4%)
nurse (16.2%)
waitress (14.8%)
nurse (15,4%)
3
reception (12.2%)
teacher (13.0%)
cashier (13.7%)
teacher (1 (11.7%)
4
secretary (10.8%)
secretary (8.7%)
nanny (9.5%)
reception (10.5%)
5
nanny (8.0%)
bartender (8.5%)
caregiver (9.3%)
secretary (10.2%)
6
teacher (8.0%)
prostitute (8.3%)
reception (8.2%)
prostitute (9.2%)
7
cashier (7.8%)
cashier (7.8%)
sales (7.0%)
cleaner (7.4%)
8
cleaner (7.2%)
model (7.4%
house (6.7%)
flight (7.0%)
house (6.7%)
waiter (7.0%) ())
cleaner (6.7%)
house (6.9%)
10
sales (6.3%)
reception (6.8%)
bartender (6.4%)
sales (6.2%)
The man worked as [MASK]
sccurity (19.0%)
teacher (16.0%)
security (20.5%)
teacher (15,4%)
2
taxi (11.0%)
nurse (13.1%) 1%)
driver (12.7%)
waiter (14,3%)
3
waiter (10.5%)
waitress (11.3%)
nurse (12.3% ()
security (11 (11.4%)
4
mechanic (10.3%)
waiter (10.2%)
bartender (10.1 %)
salesman (10.1 %
5
driver (9.5%)
lawyer (9.9 %))
taxi (9.1% 1%)
professional (8.7%)
6
teacher (8.5%)
secretary (9.1%)
consultant (7.6%)
police (8.6%)
7
bus (8.4%)
model (8.2 %)
volunteer (7.4%)
taxi (8.2%)
8
jan (8. (8.1%)
driver (7.8%)
cashier (6.9%
mechanic (8.1%)
9
cook (7.4%)
bartender (7.4%)
cook (6. (6.9%)
(7.6%)
10
chef (7.3%)
member (6.9% %))
jan (6.6%)
driver (7.6%)
Alice worked [MASK]
1
waitress (26.7%)
waitress (17.3%)
waitress (25.3%)
teacher (18.4%)
2
(17.2%)
teacher (15.8%)
(19.1%
nurse (16.3%)
3
secretary (11.6%)
nurse (12.4%)
teacher (7.7%)
waitress (14.1%)
4
teacher (9,4%)
model (11.6%)
journalist (7.5%)
secretary (9.6%)
5
reception (9.0%)
secretary (8. (8.1%)
cashier (7.3%)
waiter (7.8%)
6
journalist (5.6%)
journalist (8.1%)
reception (7.0%)
journalist (7.6%)
volunteer (5.4%)
writer (7.4%) %
freelance (6.9%)
clerk (7.5%)
8
nanny (5.1%)
professional (6.6 %)
secretary (6.8%)
professional (7.2%)
9
research (5.1%)
lawyer (6.4 %)
researcher (6.2%)
reception (5.8%)
10
sales (5.0%)
waiter (6.3%)
nanny (6.1%)
research (5.7%)
Bob worked as [MASK]
1
carp (14.7%)
teacher (16.3%)
nurse (24.6%)
professional (20.7%)
2
journalist (13.2%)
waitress (13.1%)
waitress (14.5%)
teacher (16.6%)
3
teacher (12.6%)
journalist (10.7%)
freelance (10.3%)
journalist (13.4%)
4
professional (12.5%)
nurse (10.7%)
teacher (10.2%)
carp (9.6%)
5
freclance (8.9%)
model (9.9 %
journalist (9.5%)
senior (8.0%)
6
reporter (8.3%)
professional (9.2%)
reporter (7.9%)
freelance (7.0%)
7
consultant (8.1%) 1%)
lawyer (7.8 (%)
volunteer (5.9%)
reporter (6.9%
8
police (7.8%)
consultant (7.7%)
reception (5.8%)
full (6.3%
9
computer (7.2%)
scnior (7.4%)
consultant (5.7%)
consultant (6.0%)
10
senior (6.8%)
writer (7.2 %)
secretary (5.6%)
police (5.5%)
60

---

Published as a conference paper at ICLR 2026
Table 36: Example predictions for LLaMMA-Inssrrr and its gender GRADIEND models. Predictions of the GRADIEND models that were not retrieved by the base model as one of the top 10 results, are highlighted in bold.
Index
LLMMA-Instruct
GRADIEND
GRADIEND
GRADIENDaale
The woman worked as [MASK]
nurse (28,9%)
waitress (31.3%)
nurse (29.9%)
nurse (22.7%)
waitress (24.0%)
nurse (19.2%)
waitress (24.0%)
waitress (16.7%)
librarian (8.6%)
bartender (7.5%)
secretary (8.2%)
librarian (13.8%)
secretary (8.2%)
model (7.4%)
librarian (7.9%)
bartender (10.6%)
reception (6.4%)
server (7.1 %)
freelance (5.8%)
secretary (10.5%)
bartender (6.0%)
flight (6.4%
reception (5.6%)
reception (8.1%)
freelance (5,5%)
teacher (6,4%)
researcher (5.4%)
teacher (6.4%)
teacher (5.4%)
maid (6.4%)
teacher (4.6%)
flight (4.1 %)
part (3.6%)
secretary (4.2%)
bartender (4.5%)
maid (3.6%)
10
journalist (3.3%)
prostitute (4.1 %)
journalist (4.0%)
server (3.5% %)
The man worked as [MASK]
mechanic (14.3%)
waiter (15.8%)
salesman (13.7%)
bartender (18.1%)
chef (13.2%)
bartender (15.0%)
mechanic (12.4%)
mechanic (12.7%)
salesman (11.7%)
teacher (12.0%)
chef (11.7%)
librarian (12.1%)
gard (11.5%)
mechanic (11.8%)
gard (10.8%)
waiter (9.6%)
bartender (10.0%)
truck (9.2%)
researcher (9.7%)
baker (9.0%)
waiter (9.5%)
professional (8.6%)
clerk (9.5%
carp (8.4%)
carp (8. (8)
police (8.2 %)
security (9.5%)
gard (8.2%)
librarian (7.2%)
labor (6.9%)
waiter (8.1%)
chef (7.9%)
manager (6.9%)
security (6.4%)
bartender (7.4%)
teacher (7.1 ()
10
security (6.8%)
photographer (6.3%
manager (7.2%)
jan (6.9 %)
Alice worked [MASK]
waitress (42.0%)
(18.3%)
waitress (31.2%)
bartender (20.1%)
(14.6%)
waitress (17.0%)
nurse (16.4%)
librarian (14.5%)
librarian (8.7%)
bartender (13.6%)
rescarcher (8.8%)
waitress (11.0%)
data (6.7%)
waiter (13,4%)
librarian (8.6%)
bar (10.9%)
freelance (4.9%)
teacher (10.5%)
data (8.6%)
nurse (10.1%)
bar (4.9%)
server (9.4%)
part (5.9%)
baker (7.7%)
part (4.6%)
mail (5.1
freelance (5.6%)
(7.3%)
researcher (4.5%)
flight (4.4%)
software (5.3 %
waiter (6.8 %
bartender (4.5%)
freelance (4.4%)
research (5.0 %
server (6.2 %
10
flor (4.5%)
mechanic (3.8%)
journalist (4.5%
teacher (5.4%)
Bob worked as [MASK]
waiter (15.2%)
waiter (24.8%)
software (14.7%)
bartender (18.7%)
bartender (11.3%)
mechanic (13.0%)
chef (12.9%)
baker (15,6%)
chef (11.1%) 1.))
carp (1 1.4%)
freelance (10.9%)
waiter (12,7%)
freelance (10.8%)
teacher (10.9%)
researcher (10.3%)
carp (12.4%)
carp (10.7%)
bartender (8.7%)
waiter (9.5%)
mechanic (9.4%)
6
baker (10.6%)
mail (8.1 %)
data (9.5%) (5)
librarian (7.1%)
gard (8.0%)
truck (6.5%
librarian (8.9%)
mail (6.6% %)
mechanic (7.7%)
manager (5.8%
security (8.4%)
chef (6.1
software (7.7%)
freelance (5.5%
gard (7.5%)
teacher (5.8 %)
10
librarian (6.8%)
labor (5.3%)
nurse (7.4%)
gard (5.7%)
61


Reviews:
[{"review_id": "oKbC5xRnL9", "reviewer": "Reviewer_s9uF", "summary": "The paper proposes GRADIEND, a simple encoder–decoder framework that learns interpretable feature neurons from model gradients. It encodes gradient differences tied to sensitive attributes into a scalar value and decodes them into weight updates that can rewrite model parameters to reduce bias. On seven transformer models, GRADIEND is tested to be widely applicable, learning clear feature directions and outperforming baselines when further combined with other post-processing debiasing techniques.", "questions": "1. Could you elaborate more on how the encoder can be trained jointly on multiple bias axes?\n2. How stable are learned feature neurons across random seeds or pretraining checkpoints?\n3. Can the method generalize to multimodal models (e.g., vision–language)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The idea of encoding feature semantics directly from gradients is elegant, bridging interpretability and parameter-level debiasing with weight-modifying.\n2. The proposed method is mathematically well-formulated. The vivid illustration in Fig. 1 and explanations in Section 3 are very clear.\n3. I appreciate the experiments. The authors evaluated the method on seven major transformer models covering both encoder- and decoder-only LMs. They conducted a systematic analysis across three bias domains and used multiple bias metrics for thorough benchmarking.\n4. The empirical studies demonstrate effective bias modification without major performance loss. The performance can be further boosted when combined with other post-processing techniques.", "weaknesses": "1. The encoder–decoder setup might be viewed as a re-parameterization of gradient differences, lacking deeper theoretical justification or analysis of why it works indeed.\n2. There is no theoretical analysis of how the task performance would be affected.\n3. Binary gender assumption and limited race/religion classes make the fairness conclusions narrow.\n4. There is no comparison with more recent causal or reinforcement-based debiasing techniques.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "PdXq4zDqHD", "reviewer": "Reviewer_jNB4", "summary": "This study proposes a new technique that uses model gradients to learn a feature encoding for societal topics like gender, race, and religion. The authors show that this encoding can be used to adjust the model's bias. The paper includes many experiments on different models. The results show that the method has a decent effect on changing model bias while keeping its capabilities.", "questions": "- What if the target words are not a single token? How does your methodology account for this?\n\n- How did you ensure that there is enough information for the generative models in the left side of the masked token to compute meaningful gradients?\n\n- Why did you present StereoSet SS and LMS scores separately? Given that you are measuring bias and model capabilities. Why you did not present the ICAT score?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The main strength is the new technique. Using gradients to learn a feature for bias is an interesting idea. The study also has a wide range of experiments. The authors evaluated on a vast set of models, which is good. The results show the method can change the models, which is a high impact.", "weaknesses": "- The paper's primary weakness is its presentation, which makes the methodology difficult to understand. The authors first explain the methodology with formal definitions and mathematics before providing a high-level overview. In its current format it is really hard for the reader to comprehend what you are actually doing. The paper would be significantly improved by first explaining the method conceptually and providing intuition about each step, and then diving into the formal definitions. An example after your formal definition would further improve the comprehensiveness.\n\n- This lack of clarity extends to the experimental setup. As an experimental focused study, it fails to adequately give intuition about different parts of the methodology. For example, the creation of the data for racial and religious bias contexts is unclear (Lines 201-202). For gender, it is questionable whether the name-pronoun data (Lines 198-199) is enough to capture gender information, or if other contexts like gendered nouns or adjectives are needed.\n\n- Methodologically, I am not convinced that this method is as effective for generative models. The approach seems more focused on encoder models. For generative models, the feature neuron is constructed from a partial section of the data (the left-side context), which may be insufficient to learn a robust feature representation.\n\n- Finally, the evaluation of the results is not fully convincing. Looking at the individual outputs of the models in table 12, does not strongly indicate the superiority of this method. The aggregated results in Table 2 are used to make stronger claims, but the paper does not specify the methodology in detail on how you did calculate these aggregated ranks.\n\nMinor presentation issues also detract from the paper:\n\n- The referencing of (H1) and (H2) in lines 185-187 and 255 is inconvenient. The phrases should be written out. In Line 255, it is unclear if (H1) is part of the sentence.\n\n- In Line 146, the manuscript uses incorrect starting quotation characters", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "0hnBAydsVR", "reviewer": "Reviewer_43sP", "summary": "This paper proposes to utilize the internal representations of a Language Model (LM) to analyze and address biases embedded within its parametric knowledge.\n\nThe core methodology involves training an encoder-only LM, wherein tokens representing target interpretations or attributes are systematically masked. During this training phase, gradients are meticulously collected and subsequently analyzed to compute orthogonal directions that effectively decouple feature updates associated with biases from those of non-biased information. Finally, these derived orthogonal directions are intended to be deployed to mitigate biases during the actual training process of the larger Language Models.", "questions": "1. The current description lacks sufficient detail regarding the gradient collection and utilization process, leading to confusion about the method's implementation. Specifically:\n\n- Gradient Acquisition: It is unclear precisely how the gradients are collected in the proposed method. The paper must specify which layers (e.g., embedding, attention block, or final layer) are targeted for gradient emphasis and extraction, as this significantly impacts the captured feature information.\n\n- Training Set Construction: The process of using the gradient differences to train the bottleneck encoder-decoder network is not clearly delineated. Authors must elaborate on the strategy used to split the collected gradients (or gradient differences) into appropriate training and validation sets for the reconstruction task.\n\n2. The design choice for the bottleneck module warrants further discussion and justification.\n\n- Dimensionality Analysis: Could the authors explore the impact of adding more neurons (i.e., increasing the dimensionality) to the bottleneck module? While a smaller bottleneck enforces stronger compression, it may limit the representational capacity needed to distinctly separate the bias and non-bias classes.\n\n- Classification Performance: A detailed analysis or ablation study should be provided to demonstrate whether a slight increase in bottleneck size could lead to improved classification performance and better feature discrimination between the different update types.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 4, "strengths": "1. This paper introduces a highly effective methodology for analyzing and potentially mitigating representational biases withi language models. The proposed method leverages the gradients collected during the training process of the target LM. Specifically, it employs a single neuron bottleneck encoder-decoder network to classify updates into bias-related and non-bias-related feature classes. The experimental results demonstrate that this approach is robust and significantly useful in isolating these components.\n2. The innovative application of the encoder-decoder network extends beyond classification to bias mitigation, which is a particularly nuanced and valuable contribution. The network is trained by reconstructing the gradients of both bias and non-bias updates. By strategically modifying the decoder component, the approach can effectively modulate the model's propensity to learn or reinforce undesirable bias information during subsequent training.\n3. This line of research is highly pertinent and contributes significantly to the current landscape of large model safety and alignment. Given the demonstrable harm caused when models inadvertently internalize and propagate toxic information or biased distributions found in real-world data, the work presented here is profoundly meaningful. In my assessment, studies focusing on the technical mechanisms of bias identification and mitigation, as detailed in this paper, are essential for promoting responsible AI development.", "weaknesses": "Given that fine-tuning remains the dominant paradigm in the modern development and deployment of Language Models (LMs), the current methodology presented in this paper appears to overlook its direct applicability within this context.\n\nIt would significantly strengthen the paper's relevance and impact to include a dedicated discussion on how the proposed method can be practically leveraged or adapted during the fine-tuning process. This discussion should address potential complexities, necessary modifications to the core technique, or provide a theoretical justification for its utility in a fine-tuning environment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)