An Investigation of Structures Responsible for Gender Bias in BERT and DistilBERT

Leteno, Thibaud; Gourru, Antoine; Laclau, Charlotte; Gravier, Christophe

doi:10.1007/978-3-031-30047-9_20

Cited by 3 publications

References 18 publications

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Regional bias in monolingual English language models

Lyu,

Dost,

Koh

et al. 2024

Mach Learn

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In Natural Language Processing (NLP), pre-trained language models (LLMs) are widely employed and refined for various tasks. These models have shown considerable social and geographic biases creating skewed or even unfair representations of certain groups. Research focuses on biases toward L2 (English as a second language) regions but neglects bias within L1 (first language) regions. In this work, we ask if there is regional bias within L1 regions already inherent in pre-trained LLMs and, if so, what the consequences are in terms of downstream model performance. We contribute an investigation framework specifically tailored for low-resource regions, offering a method to identify bias without imposing strict requirements for labeled datasets. Our research reveals subtle geographic variations in the word embeddings of BERT, even in cultures traditionally perceived as similar. These nuanced features, once captured, have the potential to significantly impact downstream tasks. Generally, models exhibit comparable performance on datasets that share similarities, and conversely, performance may diverge when datasets differ in their nuanced features embedded within the language. It is crucial to note that estimating model performance solely based on standard benchmark datasets may not necessarily apply to the datasets with distinct features from the benchmark datasets. Our proposed framework plays a pivotal role in identifying and addressing biases detected in word embeddings, particularly evident in low-resource regions such as New Zealand.

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Regional bias in monolingual English language models

Lyu,

Dost,

Koh

et al. 2024

Mach Learn

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Reducing Bias in Sentiment Analysis Models Through Causal Mediation Analysis and Targeted Counterfactual Training

Da,

Bossa,

Berenguer

et al. 2024

IEEE Access

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Large language models provide high-accuracy solutions in many natural language processing tasks. In particular, they are used as word embeddings in sentiment analysis models. However, these models pick up on and amplify biases and social stereotypes in the data. Causality theory has recently driven the development of effective algorithms to evaluate and mitigate these biases. Causal mediation was used to detect biases, while counterfactual training was proposed to mitigate bias. In both cases, counterfactual sentences are created by changing an attribute, such as the gender of a noun, for which no change in the model output is expected. Biases are detected and eventually corrected each time the model behaviour differs between the original and the counterfactual sentence. We propose a new method for de-biasing sentiment analysis models that leverages the causal mediation analysis to identify the parts of the model primarily responsible for the bias and apply targeted counterfactual training for model de-biasing. We validated the methodology by fine-tuning the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for sentiment prediction. We trained two sentiment analysis models using the Stanford Sentiment Treebank dataset and the Amazon Product Reviews, respectively, and we evaluated the fairness and prediction performances using the Equity Evaluation Corpus. We illustrated the causal patterns in the network and showed that our method achieves both high fairness and more accurate sentiment analysis than the stateof-the-art approach. Contrary to state-of-the-art models, we achieved a noticeable improvement in gender fairness without hindering sentiment prediction accuracy.

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Regional Bias in Monolingual English Language Models

Lyu,

Dost,

Koh

et al. 2024

Preprint

View full text Add to dashboard Cite

In Natural Language Processing (NLP), pre-trained language models (LLMs) are widely employed and refined for various tasks. These models have shown considerable social and geographic biases creating skewed or even unfair representations of certain groups.Research focuses on biases toward L2 (English as a second language) regions but neglects bias within L1 (first language) regions.In this work, we ask if there is regional bias within L1 regions already inherent in pre-trained LLMs and, if so, what the consequences are in terms of downstream model performance.We contribute an investigation framework specifically tailored for low-resource regions, offering a method to identify bias without imposing strict requirements for labeled datasets. Our research reveals subtle geographic variations in the word embeddings of BERT, even in cultures traditionally perceived as similar. These nuanced features, once captured, have the potential to significantly impact downstream tasks. Generally, models exhibit comparable performance on datasets that share similarities, and conversely, performance may diverge when datasets differ in their nuanced features embedded within the language. It is crucial to note that estimating model performance solely based on standard benchmark datasets may not necessarily apply to the datasets with distinct features from the benchmark datasets. Our proposed framework plays a pivotal role in identifying and addressing biases detected in word embeddings, particularly evident in low-resource regions such as New Zealand.

show abstract

An Investigation of Structures Responsible for Gender Bias in BERT and DistilBERT

Cited by 3 publications

References 18 publications

Regional bias in monolingual English language models

Regional bias in monolingual English language models

Reducing Bias in Sentiment Analysis Models Through Causal Mediation Analysis and Targeted Counterfactual Training

Regional Bias in Monolingual English Language Models

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