Papers with WEAT
Universal Sentence Encoder for English (D18-2)
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Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil
| Challenge: | TensorFlow Hub sentence embedding models have good task transfer performance . model variants allow for trade-offs between accuracy and compute resources . |
| Approach: | They propose easy-to-use TensorFlow Hub sentence embedding models with good task transfer performance. |
| Outcome: | The proposed models outperform models without transfer learning and those that use only word-level transfer on a number of NLP tasks. |
Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling (2022.coling-1)
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| Challenge: | Existing studies have highlighted a variety of biases in pre-trained language models . however, these studies focus on fine-grained analysis of educational corpora and text that is not English . |
| Approach: | They analyze bias across text and through multiple architectures on a corpus of 9,165 German peer-reviews collected from university students over five years. |
| Outcome: | The proposed dataset shows that pre-trained language models exhibit conceptual, racial, and gender biases. |
No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media (2022.findings-emnlp)
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| Challenge: | Recent work has relied on word embedding bias measures, such as WEAT, but these methods can be inaccurate due to several representation issues, such low-resource settings and token frequency differences. |
| Approach: | They propose to use WEAT to quantify social bias in US online news articles and embed embedding algorithms to account for the aforementioned issues. |
| Outcome: | The proposed algorithms do not match the literature, but they reduce the gap. |
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings (P19-1)
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| Challenge: | Word embeddings suffer from unintended demographic biases, a new study shows . word embedders can cause downstream NLP systems to be unfair, the authors argue . |
| Approach: | They propose a metric to evaluate the fairness of word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
| Outcome: | The proposed metric measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
Understanding Undesirable Word Embedding Associations (P19-1)
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| Challenge: | Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. |
| Approach: | They propose to use subspace projection to debias vectors post hoc using a model that implicitly does matrix factorization to debunk gender bias. |
| Outcome: | The proposed test overestimates gender bias in word embeddings by using subspace projection, a method that is widely used in training. |
How Gender Debiasing Affects Internal Model Representations, and Why It Matters (2022.naacl-main)
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| Challenge: | Existing studies of gender bias in NLP focus on extrinsic or intrinsic bias, but the relationship between extrindic and intrinsic bias is relatively unknown. |
| Approach: | They propose a framework to measure extrinsic and intrinsic bias together and propose metric to measure debiasing and intrinsic debiases. |
| Outcome: | The proposed framework provides a comprehensive perspective on bias in NLP models, which can be applied to deploy NLP systems in a more informed manner. |
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. |
| Approach: | They investigate gender and racial bias across pre-trained language models . they evaluate bias within pre-trainers using three metrics: WEAT, sequence likelihood, and pronoun ranking. |
| Outcome: | The proposed model fails to detect gender and racial biases in pre-trained models . the model is ineffective when word embedding, demonstrating the need for more robust bias testing in transformers. |
Measuring bias in Instruction-Following models with P-AT (2023.findings-emnlp)
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| Challenge: | Instruction-Following Language Models (IFLMs) are promising and versatile tools for solving many downstream, information-seeking tasks. |
| Approach: | They propose a resource to test whether IFLMs are prone to biases . they cast WEAT word tests in promptized classification tasks and associate a metric - the bias score . |
| Outcome: | The proposed resource consists of 2310 prompts and tests gender and race biases in all the analyzed models. |
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly utilized in educational tasks such as providing writing suggestions to students. |
| Approach: | They conduct a large-scale user study with 231 students writing business case peer reviews in german. |
| Outcome: | The proposed model does not carry bias in the feedback loops of the students . |
Global Voices, Local Biases: Socio-Cultural Prejudices across Languages (2023.emnlp-main)
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| Challenge: | Existing studies on human biases are heavily skewed towards Western and European languages . despite growing interest in language models, there are several shortcomings in the literature . |
| Approach: | They scale the Word Embedding Association Test to 24 languages and add culturally relevant information for each language. |
| Outcome: | The proposed language models can reflect and often amplify the effects of bias across linguistic, cultural, and societal borders. |