A Study of Implicit Bias in Pretrained Language Models against People with Disabilities (2022.coling-1)
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| Challenge: | Pretrained language models exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biase in language technologies. |
| Approach: | They propose to use word embedding-based and transformer-based PLMs to test for the presence of biases against people with disabilities (PWDs) |
| Outcome: | The proposed models favor ableist language, despite their sociodemographic biases against race and gender. |
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| Challenge: | Pretrained language models (PLMs) propagate social stigmas and stereotypes, a critical concern given their widespread use. |
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| Challenge: | Statistically significant results demonstrate that people with disabilities can be disadvantaged. |
| Approach: | They used a large-scale BERT language model to predict word predictions and found that people with disabilities can be disadvantaged. |
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A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models (2023.emnlp-main)
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| Challenge: | Various types of social biases have been reported with pretrained Masked Language Models (MLMs) in prior work. |
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Social Biases in NLP Models as Barriers for Persons with Disabilities (2020.acl-main)
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| Challenge: | toxicity prediction and sentiment analysis models perpetuate undesirable social biases from the data on which they are trained. |
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StereoSet: Measuring stereotypical bias in pretrained language models (2021.acl-long)
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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: | Hundreds of studies have highlighted ethical issues in NLP models . |
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Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers (2021.naacl-main)
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| Challenge: | Existing pre-trained language models are not fully considered for societal biases . pre-training models can be useful for many NLP tasks, but they can be harmful when used at scale. |
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| Challenge: | Pretrained Language Models (PLMs) are widely used in NLP for various tasks. |
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Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages (2025.findings-naacl)
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| Challenge: | Existing studies do not focus on linguistically grounded attacks, but pre-trained models are susceptible to these perturbations. |
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Assessing Combinational Generalization of Language Models in Biased Scenarios (2022.aacl-short)
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| Challenge: | Existing work focuses on assessing in-domain knowledge, but shedding light on what pre-trained Language Models learn is important. |
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