UnMASKed: Quantifying Gender Biases in Masked Language Models through Linguistically Informed Job Market Prompts (2024.eacl-srw)
Copied to clipboard
| Challenge: | Language models (LMs) often include societal biases encoded in the human-produced datasets used for their training. |
| Approach: | They evaluated six prominent language models: BERT, RoBERTa, DistilBERT, BERT- multilingual, XLM-RoBERT and DistilberT- multilinguistic. |
| Outcome: | The results show that the models generated by the models were stereotypically gendered and with a reduced bias in multilingual variants. |
Similar Papers
Gender Bias in Masked Language Models for Multiple Languages (2022.naacl-main)
Copied to clipboard
| Challenge: | Masked Language Models (MLMs) pre-trained by predicting masked tokens on large corpora have been used successfully in natural language processing tasks for a variety of languages. |
| Approach: | They propose to use English attribute word lists to evaluate bias in eight languages without manually annotating data. |
| Outcome: | The proposed model significantly correlates with the existing English datasets for gender bias. |
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models (2022.findings-naacl)
Copied to clipboard
| Challenge: | Pretrained language models (PLMs) have been shown to encapsulate social biases, including those relating to gender and race. |
| Approach: | They propose a new bias measure based on Jensen–Shannon divergence that retains more information from the model output probabilities than other previously proposed bias measures. |
| Outcome: | The proposed measure outperforms CrowS-Pairs and other similar measures for non-English datasets. |
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to mitigate human-like biases in pretrained language models are based on external corpora and require a distribution alignment loss to mitigate them. |
| Approach: | They propose an automatic method to mitigate biases in pretrained language models by searching for biased prompts such that cloze-style completions are the most different with respect to different demographic groups. |
| Outcome: | The proposed method reduces biases in pretrained language models, including gender and racial bias, and improves fairness of the models. |
On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited. |
| Approach: | They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models. |
| Outcome: | The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models. |
MISGENDERED: Limits of Large Language Models in Understanding Pronouns (2023.acl-long)
Copied to clipboard
| Challenge: | excluding non-binary gender identities can perpetuate harm against non-bisexual individuals through exclusion and marginalization. |
| Approach: | They propose a framework for evaluating large language models’ ability to correctly use preferred pronouns. |
| Outcome: | The proposed framework evaluates language models' ability to correctly use preferred pronouns in English. |
In-Contextual Gender Bias Suppression for Large Language Models (2024.findings-eacl)
Copied to clipboard
| Challenge: | Prior work has proposed debiasing methods that require human labelled examples, data augmentation and fine-tuning of LLMs, which are computationally expensive. |
| Approach: | They propose to suppress gender biases by providing textual preambles from manually designed templates and real-world statistics without accessing model parameters. |
| Outcome: | The proposed methods suppress gender biases in English LLMs using a CrowsPairs dataset without accessing model parameters. |
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias (2020.emnlp-main)
Copied to clipboard
| Challenge: | English challenge datasets highlight gender-ambiguous occurrences of ‘doctor’ as male doctors, but they are not useful for other languages. |
| Approach: | They propose to build multi-task challenge datasets for detecting gender bias that lead to unambiguously wrong model predictions for languages with type B reflexivization. |
| Outcome: | The proposed dataset can detect gender bias in languages with type B reflexivization and spans four languages and four NLP tasks. |
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models use cultural biases implicitly, causing harm . identifying and quantifying learnt biase enables us to measure progress . |
| Approach: | They propose a benchmark to measure social bias in pretrained language models . they use 1508 examples that cover stereotypes dealing with nine types of bias . |
| Outcome: | The proposed benchmark focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. |
Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique (2022.aacl-short)
Copied to clipboard
| Challenge: | In this paper, we analyze the variations in gender and racial biases in BERT-like language models when exposed to different demographic groups. |
| Approach: | They analyze gender and racial biases in BERT-like language models when exposed to different demographic groups. |
| Outcome: | The proposed model can mitigate biases in text authored by disadvantaged demographic groups compared to advantaged groups . the proposed model is agnostic to the language of the speakers behind the language . |
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets. |
| Approach: | They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments. |
| Outcome: | The proposed method extends the existing dataset to 108K diverse English sentences. |