Unlearning Bias in Language Models by Partitioning Gradients (2023.findings-acl)
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| Challenge: | Recent research has shown that large-scale pretrained language models exhibit issues relating to racism, sexism, religion bias, and toxicity in general. |
| Approach: | They propose a gray-box method for debiasing pretrained masked language models using partitioned contrastive gradient unlearning (PCGU) aims to optimize only the weights that contribute most to a specific domain of bias by computing a first-order approximation based on the gradients of contrastive sentence pairs. |
| Outcome: | The proposed method is low-cost and can pinpoint the sources of social bias in large pretrained language models. |
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Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khattak
| Challenge: | Existing methods for mitigating bias in language models are expensive and time-consuming . comparative studies have not evaluated their respective advantages and disadvantages . |
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| 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. |
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| Challenge: | Mitigating gender bias in NLP has a long history tied to debiasing static word embeddings. |
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| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
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| Challenge: | Recent debiasing approaches target different demographic groups, harming fairness and discrimination. |
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Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models (2022.acl-long)
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| Challenge: | Large language models and other massively pre-trained "foundation" models can easily adapt to a wide variety of downstream tasks in a process called finetuning. |
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| Challenge: | Existing literature on stereotypical biases in language models is limited . current evaluations focus on measuring bias without considering language modeling ability . |
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| Challenge: | Existing methods to reduce biases in pre-training models are hampered by their performance. |
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| Challenge: | Pretrained language models pick up and reproduce undesirable biases when trained on large, unfiltered crawls from the Internet. |
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| Challenge: | a new method to mitigate stereotypical bias in large language models is needed . inherent biases from training on vast Internet datasets can amplify harmful stereotypes . |
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