| Challenge: | Existing methods to reduce gender bias in natural language are costly and time-consuming. |
| Approach: | They propose a method to generate gender variants for a given text using pre-trained language models as the resource without any task-specific labelled data. |
| Outcome: | The proposed method can reduce gender bias in a language generation context without a task-specific labelled data. |
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Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions (2023.acl-short)
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| Challenge: | Existing approaches to de-bias pre-trained large language models focus on changes to training regime, but this is not feasible. |
| Approach: | They propose to de-bias a pre-trained model by fine-tuning it on only 10 examples . they show that the technique performs better than competitive baselines . |
| Outcome: | The proposed method performs better than competitive state-of-the-art baselines with minimal loss in language modeling ability. |
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model (2023.acl-long)
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| Challenge: | Existing work has explored using sequence-to-sequence rewriting models to transform biased outputs into more gender-fair language by creating pseudo training data through linguistic rules. |
| Approach: | They propose to use machine translation models to create gender-biased text from real gender-fair text via round-trip translation to eliminate rule-based data creation. |
| Outcome: | The proposed approach matches the performance of state-of-the-art rewriting models for English. |
Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function (P19-2)
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| Challenge: | Existing methods to reduce gender bias in natural language datasets are inadequate. |
| Approach: | They propose a loss function modification approach which equalizes the probabilities of male and female words in the output. |
| Outcome: | The proposed approach outperforms existing methods in several aspects, especially in reducing gender bias in occupation words. |
Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Existing methods for debiasing are resource-intensive and costly. Existing solutions for debiansing require fine-tuning on downstream tasks. |
| Approach: | They propose to integrate Masked Language Modeling (MLM) training objectives into fine-tuning’s training process to debiase the PLMs. |
| Outcome: | The proposed approach outperforms the state-of-the-art baselines in terms of gender bias scores while improving PLMs’ performance solely using the downstream tasks’ dataset. |
Reducing Gender Bias in Abusive Language Detection (D18-1)
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| Challenge: | Abusive language detection models tend to be biased toward identity words of a certain group of people . recent studies have raised concerns about the robustness of such systems . |
| Approach: | They propose to use debiased word embeddings, gender swap data augmentation to reduce model bias . they also propose to fine-tune models with a larger corpus to correct such bias if needed . |
| Outcome: | The proposed methods reduce model bias by 90-98% and can be extended to correct model bias in other scenarios. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
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| 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. |
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020.emnlp-main)
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| Challenge: | Recent research shows that dialogue systems trained on human conversation data are biased and can produce responses that reflect people’s gender prejudice. |
| Approach: | They propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance. |
| Outcome: | The proposed framework significantly reduces gender bias in dialogue models while maintaining the response quality. |
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Mitigating gender bias in NLP has a long history tied to debiasing static word embeddings. |
| Approach: | They propose a masked language modelling task where content is developed around known social stereotypes and a projective debiasing method is used to reduce bias. |
| Outcome: | The proposed methods reduce intrinsic bias and mitigat observed bias in a downstream setting, but the two outcomes are not necessarily correlated. |