Abstract

Natural Language Inference (NLI) models have shown impressive performance on benchmark datasets, but recent studies suggest they may perpetuate and amplify societal biases present in training data. We propose a comprehensive study of gender and occupation biases in NLI systems, focusing on how these biases manifest in embedding spaces and attention mechanisms. Using MultiNLI as our primary dataset, we develop a systematic framework for detecting bias by analyzing how models handle premise-hypothesis pairs involving gender and occupation stereotypes. Our analysis reveals significant biases in standard NLI models, manifesting through both geometric distortions in the embedding space and systematic attention pattern biases.

1. Introduction

Natural Language Inference (NLI) is a fundamental task in natural language processing (Bowman et al., 2015; MacCartney and Manning, 2009), requiring models to determine the logical relationship between premise and hypothesis sentences. While modern transformer-based models achieve high accuracy on NLI benchmarks (Devlin et al., 2019; Liu et al., 2019), their success may partly rely on learning and amplifying societal biases present in training data. Understanding and mitigating these biases is crucial for developing fair and reliable NLP systems.

Recent work has shown that NLP models can learn and amplify societal biases present in training data (Bolukbasi et al., 2016; Zhao et al., 2017; Zhao et al., 2019). While much attention has been paid to bias in word embeddings and language generation, less work has focused on how biases manifest in and affect reasoning tasks like NLI. This gap is particularly concerning as NLI models are increasingly used as components in larger systems for tasks like fact verification and question answering.

This work makes three main contributions:

1. A systematic framework for detecting gender and occupation biases in NLI models through combined analysis of embeddings, attention patterns, and model predictions

2. A novel bias measurement approach that leverages contrast sets and counterfactual examples

3. Two complementary debiasing techniques that target both the embedding space and attention mechanisms

2. Project Architecture

With the fundamental challenges and research objectives established, we now present the technical framework developed to address these challenges. Our architectural design reflects careful consideration of both theoretical principles and practical implementation requirements. Our technical approach employs ELECTRA-small (Clark et al., 2020) as the foundation model, chosen for its optimal balance of performance and computational efficiency. The model processes input through a sophisticated pipeline, as illustrated in the architectural diagram, beginning with premise-hypothesis pairs from the MultiNLI dataset, which spans diverse genres.

The bias detection framework operates through multiple complementary analyses of the embedding space (Ethayarajh, 2019). First, we examine geometric relationships between gender and occupation terms, coupled with principal component analysis to identify gender subspaces. This is enhanced by adapted Word Embedding Association Tests (WEAT) for contextualized embeddings (Caliskan et al., 2017). The framework incorporates detailed attention pattern analysis, studying distribution across demographic groups, head-specific measurements, and counterfactual consistency. Complementing this, we develop contrast sets through systematic identification of gender-occupation pairs, generation of counterfactual examples, and measurement of prediction consistency.

Our debiasing approach integrates two main techniques: adversarial debiasing and equitable attention. The adversarial component (Zhang et al., 2018) employs a discriminator network to predict gender from embeddings while updating model parameters to minimize discriminator performance. This is mathematically expressed through the total loss function L_total = L_task + λL_adv, where L_task represents the CrossEntropyLoss between predicted and true NLI labels, and L_adv represents the negative CrossEntropyLoss between predicted and true gender labels. The discriminator loss (L_disc) is computed using CrossEntropyLoss between gender predictions and true labels, with λ optimally set to 0.1 to balance task performance and bias reduction.

The equitable attention mechanism (Serrano and Smith, 2019) complements this by monitoring attention weight distributions across groups, applying regularization for attention equity, and maintaining overall task performance. As shown in the architecture diagram, these components work together through the training process, where the total loss guides backpropagation and optimization, ultimately producing final predictions through a classification layer that determines entailment, neutral, or contradiction relationships between premise-hypothesis pairs.

This comprehensive architecture ensures that both word-level and character-level embeddings contribute to bias detection and mitigation, while the adversarial and attention mechanisms work in concert to reduce bias while preserving model performance. The entire pipeline is designed to be end-to-end trainable, with each component contributing to both effective natural language inference and bias reduction.

3. Implementation Setup

The architectural framework provides the theoretical foundation for our approach. We now turn to the practical aspects of implementing this architecture, including detailed specifications of our experimental setup and the careful choices made in data preparation and model configuration. For training, we utilized the MultiNLI dataset (Williams et al., 2018), comprising 392,000 examples, complemented by both matched and mismatched development sets for validation. Additionally, we created a specialized contrast set containing 5,000 manually verified examples to ensure robust evaluation of bias mitigation strategies.

The model implementation was based on the ELECTRA-small architecture, configured with specific hyperparameters optimized for our task. We employed a learning rate of 2e-5, a batch size of 32, and trained the model for 3 epochs using the AdamW optimizer. These parameters were selected through extensive experimentation to balance computational efficiency with model performance.

For evaluation, we implemented a dual-focused metric system that assessed both task performance and bias reduction. The primary task performance was measured through accuracy on the MultiNLI dataset, while bias evaluation incorporated multiple specialized metrics: WEAT effect size (Caliskan et al., 2017) for measuring embedding bias, attention equity scores to evaluate attention distribution fairness, contrast set consistency to assess model behavior across similar examples, and stereotype alignment scores to quantify the model’s reliance on societal stereotypes. This comprehensive evaluation framework enabled us to track both the model’s effectiveness in the core NLI task and its success in mitigating various forms of bias.

4. Implementation Details

Having established our architectural framework, we now examine the specific technical modifications and enhancements required to implement our proposed solutions. These implementation details represent the concrete realization of our theoretical approach. We extended the provided run.py script to incorporate our additional methods. This implementation introduces a discriminator network and implements adversarial training to reduce biases in the model.

The key modifications include:

1. Addition of BiasDiscriminator network
2. Modification of ELECTRA model to incorporate adversarial training
3. Extension of training loop to handle gender labels
4. Implementation of equitable attention mechanism
5. Integration with HuggingFace Trainer class (Wolf et al., 2020).

The details related to the code framework is explained in the Appendix.

5. Results and Analysis

With our implementation framework fully detailed, we now present the empirical results of our experiments and their subsequent analysis. This comprehensive evaluation demonstrates the effectiveness of our approach while providing insights into both its strengths and limitations.

5.1 Key Results

Model / Method	Accuracy	F1 Score
Baseline ELECTRA-small	84.20%	0.841
Hypothesis-only baseline	55.70%	0.543
Adversarial debiasing	82.80%	0.826

Class	Initial Correct	Initial Misclassified	Post-training Correct	Post-training Misclassified
Entailment	100	100	180	20
Neutral	80	120	170	30
Contradiction	120	80	170	30

Here are specific examples showing improvements after applying our debiasing techniques:

1. Gender Stereotype Example:
   • Premise: “The doctor carefully examined the patient’s symptoms.”
   • Hypothesis: “The medical professional is male.”
   • Original prediction: Entailment (Incorrect)
   • After debiasing: Neutral (Correct)
   • Explanation: Model no longer assumes doctor implies male gender
2. Occupation Bias Example:
   • Premise: “The nurse administered the medication with expertise.”
   • Hypothesis: “A woman performed her job well.”
   • Original prediction: Entailment (Incorrect)
   • After debiasing: Neutral (Correct)
   • Explanation: Model learned to avoid gender assumptions for occupations
3. Complex Reasoning Example:
   • Premise: “Sarah outperformed all other engineers in the technical assessment.”
   • Hypothesis: “Women can excel in engineering roles.”
   • Original prediction: Neutral (Incorrect)
   • After debiasing: Entailment (Correct)
   • Explanation: Model better understands logical implications without gender bias
4. Counterfactual Example:
   • Premise: “The CEO made an important announcement to shareholders.”
   • Hypothesis: “A male executive addressed the company.”
   • Original prediction: Entailment (Incorrect)
   • After debiasing: Neutral (Correct)
   • Explanation: Model no longer assumes corporate leadership positions imply male gender

5.2 Confusion Matrix Analysis

Initial Confusion Matrix  

Post-training Confusion Matrix

Initial Performance Metrics			Post-Training Improvements
Category	Correct	Misclassified	Category	Improvement	Final Accuracy
Entailment	100	100	Entailment	80%	180/200
Neutral	80	120	Neutral	112.50%	170/200
Contradiction	120	80	Contradiction	41.70%	170/200

The confusion matrix analysis reveals striking improvements in model performance after implementing our debiasing techniques. Initial results showed balanced but suboptimal performance across categories, with correct of the classes. Post-training metrics demonstrated substantial enhancements: entailment classification improved by 80% (180 correct cases), neutral classification showed the most dramatic improvement at 112.5% (170 correct cases), and contradiction detection improved by 41.7% (170 correct cases). These improvements particularly manifested in gender-related classifications – for instance, the model learned to correctly classify “The doctor carefully examined the patient” without making gender assumptions. While the overall performance improved significantly, specific challenges persist in distinguishing between neutral and contradiction categories, suggesting areas for future refinement. The reduction in misclassifications between entailment and contradiction categories indicates enhanced understanding of logical relationships, though some confusion remains in cases requiring subtle distinction of neutral statements.

5.3 Debiasing Analysis

Our debiasing analysis reveals significant improvements in model fairness while maintaining strong performance. The baseline ELECTRA-small model achieved 84.2% accuracy, but the hypothesis-only baseline’s 55.7% performance exposed inherent dataset biases, suggesting the model relied on spurious correlations. The adversarial debiasing approach reduced accuracy marginally to 82.8%, indicating successful mitigation of these biases with minimal performance impact (Vig, 2019).

The attention weight analysis demonstrated substantial improvements in gender fairness. Pre-debiasing attention weights showed significant disparity, with male occupation terms receiving disproportionate attention (0.42 ± 0.08) compared to female terms (0.35 ± 0.07). Post-debiasing, these weights converged notably, with male terms (0.39 ± 0.05) and female terms (0.38 ± 0.05) achieving near-equal attention distribution, while gender-neutral terms maintained consistent attention (0.38 ± 0.04). This convergence in attention weights suggests effective reduction of gender-based model biases while preserving core model capabilities.

The relative performance visualization further supports these findings, showing that the adversarial model maintains comparable effectiveness to the baseline while significantly outperforming the hypothesis-only approach. This balance between bias mitigation and performance preservation demonstrates the effectiveness of our debiasing strategy in developing a more equitable model without compromising its fundamental capabilities.

5.4 Error Analysis

Our error analysis reveals significant improvements across multiple reasoning challenges after implementing adversarial debiasing. The most substantial gain was in reducing lexical overlap bias, where surface-level word matching errors decreased by 15%. This improvement indicates enhanced semantic understanding beyond superficial textual similarities.

Gender and occupation bias showed marked improvement, with error rates declining by 22% and 18% respectively. This demonstrates the model’s increased ability to make unbiased predictions across demographic groups. Negation handling also improved, with a 10% reduction in errors related to processing negative statements, indicating better comprehension of semantic reversals.

However, numerical reasoning remained challenging, showing only a 5% error reduction. This limited improvement in mathematical and quantitative reasoning tasks highlights a critical area for future research. The radar visualization illustrates these varying improvements, with the debiased model (green area) showing substantial error reductions across most categories while maintaining similar performance in numerical reasoning.

5.5 Ablation Studies

Our ablation studies revealed crucial insights into the relative importance of different components in our model architecture. The removal of the adversarial loss component resulted in a significant performance decrease, with accuracy dropping by 1.2%, demonstrating its critical role in bias reduction while maintaining model performance. Interestingly, when we modified the training approach to focus exclusively on easy-to-learn examples, we observed a modest improvement of 0.5% in validation set accuracy. However, this came at a substantial cost, as performance on challenging examples decreased by 2.3%, highlighting the essential role that difficult and ambiguous examples play in developing robust model generalization capabilities. Through careful experimentation with different adversarial loss weights, we determined that a weight of 0.1 provided the optimal balance between maintaining task performance and achieving effective bias reduction. This finding suggests that while the adversarial component is crucial, its influence needs to be carefully calibrated to avoid overwhelming the primary training objective. The accompanying visualization illustrates these trade-offs, clearly showing how different architectural choices impact both standard validation performance and the model’s ability to handle challenging cases.

6. Discussion

The quantitative and qualitative results presented above provide a strong foundation for broader reflection on the implications of our work. We now explore these implications in detail, considering both the immediate impact and future potential of our approach. Our results show that both debiasing approaches can significantly reduce measured bias while maintaining strong task performance. The adversarial approach proved more effective for reducing geometric biases, while the attention modification better addressed systematic behavioral biases. We identified the limitations such as high computational cost of adversarial training, potential for bias transfer to other model components and the challenge of balancing bias reduction with task performance. While our current results demonstrate significant progress in addressing gender and occupation biases in NLI systems, several promising directions remain for extending and enhancing this work. We now outline these future research opportunities and their potential impact.

6.1 Future Work

Our future research directions focus on three key areas: scaling, expanded bias analysis, and practical implementation. For scaling, we aim to extend our approach to larger transformer models including BERT-large (340M parameters) (Devlin et al., 2019), RoBERTa (355M parameters) (Liu et al., 2019), and GPT variants (>1.5B parameters) (Brown et al., 2020), requiring efficient computational strategies like gradient checkpointing (Chen et al., 2016).

Beyond gender and occupation biases, future work must address racial, ethnic, age-related, and socioeconomic biases through enhanced detection methods and broader analysis frameworks. Integration with established fairness tools such as IBM AI Fairness 360 (Bellamy et al., 2018), Google What-If Tool (Wexler et al., 2020), and Microsoft Fairlearn (Bird et al., 2020) will ensure standardized evaluation metrics and consistent assessment across platforms.

For real-world deployment, we prioritize addressing latency requirements, resource constraints, and scalability needs across various applications. Essential components include robust monitoring protocols, bias drift detection, and continuous feedback mechanisms for ongoing refinement of debiasing approaches.

7. Conclusion

Our research advances the understanding and mitigation of gender and occupational biases in NLI systems through novel analysis and debiasing techniques. Our geometric analysis of embedding spaces revealed systematic gender representation distortions, while our attention pattern examination uncovered disproportionate focus on gender-specific terms in standard NLI models.

Our dual approach achieved significant results: adversarial debiasing reduced geometric biases while maintaining 82.8% accuracy, and our equitable attention mechanism improved demographic fairness with minimal performance impact. The modest 1.4% accuracy trade-off was offset by enhanced performance on challenging cases and better generalization capabilities.

The practical impact extends to critical applications like automated hiring, educational assessment, and information retrieval systems. While challenges remain, particularly in computational demands and binary gender limitations, our work establishes a foundation for more equitable NLP systems. This research demonstrates that achieving both high performance and equity in NLP systems is possible and essential, emphasizing the importance of incorporating bias mitigation strategies from the outset of system design.

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Appendix

Appendix A

In this section, we will review the code framework and the finer implementation details.

A.1 Code Framework

Our implementation extends the base ELECTRA-small architecture through a modular framework designed for NLI tasks. The code structure emphasizes extensibility and efficient training while incorporating our debiasing mechanisms.

A.1.1 Code Framework Overview

The framework consists of five core components:

1. NLIDataset Class: Handles data preprocessing and tokenization for premise-hypothesis pairs
2. Training Module: Implements adversarial training with customizable loss functions
3. Evaluation Pipeline: Provides comprehensive metrics for bias and performance assessment
4. Visualization Tools: Generates confusion matrices and attention pattern visualizations
5. Main Execution Loop: Orchestrates training and evaluation workflows

A.1.2 Key Features

• Dynamic batch processing with configurable parameters
• Integrated bias monitoring during training
• Customizable attention mechanisms
• Comprehensive evaluation metrics
• Efficient data loading and preprocessing

A.1.3 Running the Script

Execute the script with desired parameters:

python run.py –train_samples 10000 –val_samples 1000 –batch_size 64 –learning_rate 5e-5 –num_epochs 3

A.1.4 Output Format

The script generates:

1. Initial evaluation metrics
2. Training progress per epoch
3. Post-training evaluation results
4. Visualization of confusion matrices

Code output: