Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work has established that a person’s demographics and speech style affect how well speech processing models perform for them. |
| Approach: | They propose a method to detect bias in pre-trained models by using word embedding association tests in natural language processing to quantify bias in models' representations of different concepts. |
| Outcome: | The proposed method detects bias in pre-trained models and can have real-world effects. |
Similar Papers
StereoSet: Measuring stereotypical bias in pretrained language models (2021.acl-long)
Copied to clipboard
| Challenge: | Existing literature on stereotypical biases in language models is limited . current evaluations focus on measuring bias without considering language modeling ability . |
| Approach: | They propose to measure stereotypical biases in four domains: gender, profession, race, and religion . they compare stereotypical and language modeling ability of popular models like BERT, GPT-2, RoBERTa and XLnet . |
| Outcome: | The proposed model shows strong stereotypical biases in gender, profession, race, and religion domains. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
Copied to clipboard
| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
On Measuring Social Biases in Sentence Encoders (N19-1)
Copied to clipboard
| Challenge: | Word embeddings such as word2vec and GloVe exhibit human-like implicit biases based on gender, race, and other social constructs. |
| Approach: | They propose a simple generaliza test to measure bias in word embeddings by comparing two sets of target-concept words to two sets . |
| Outcome: | The proposed test shows that word2vec and word2Ve exhibit human-like implicit biases based on gender, race, and other social constructs. |
T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in text-to-image generative models have produced high quality images with a breakthrough of inference speed. |
| Approach: | They propose a text-to-image association test framework that quantifies implicit stereotypes between concepts and valence and those in images. |
| Outcome: | The proposed framework quantifies implicit stereotypes between concepts and valence and those in images. |
Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models (2022.acl-long)
Copied to clipboard
| 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. |
| Approach: | They propose to use the bias transfer hypothesis to reduce social biases internalized by large language models during pre-training into harmful task-specific behavior after fine-tuning. |
| Outcome: | The bias transfer hypothesis is the theory that social biases internalized by large language models during pre-training transfer into harmful task-specific behavior after fine-tuning. |
The (Undesired) Attenuation of Human Biases by Multilinguality (2022.emnlp-main)
Copied to clipboard
| Challenge: | odor pleasantness perception is universal, but cultural biases are not always present in embedding models . et al., 2018: a new study shows that cultural bias is not always the case in embedded models based on human texts . |
| Approach: | They propose multilingual cultural aware tests to quantify biases in embedding models . they find that biased models are more likely to be multilingual than monolingual ones . |
| Outcome: | The results show that human preferences are not always universal . they also show that multilinguality reverses biases, despite differences in training corpus . |
The Risk of Racial Bias in Hate Speech Detection (P19-1)
Copied to clipboard
| Challenge: | Annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. |
| Approach: | They propose *dialect* and *race priming* as ways to reduce the racial bias in hate speech detection models by detecting differences in dialects in annotated tweets. |
| Outcome: | The proposed models acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. |
A Study of Implicit Bias in Pretrained Language Models against People with Disabilities (2022.coling-1)
Copied to clipboard
| 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. |
Marked Attribute Bias in Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing tests for gender-biased word embeddings do not address marked attribute bias . authors propose a new type of intrinsic bias measure for static word embeds . |
| Approach: | They propose a method to detect gender-biased word embeddings in a downstream NLP application . they propose 'debiasing' method to measure the marked attribute bias in embeddable word embeds . |
| Outcome: | The proposed method achieves best results on the marked attribute bias test set. |