Papers by Kathleen Fraser
How Does Stereotype Content Differ across Data Sources? (2024.starsem-1)
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| Challenge: | Existing studies of stereotypes using rating scales capture beliefs and opinions about different social groups. |
| Approach: | They compare stereotype-relevant measures of social group social status with traditional scales and a word-list generation task using free-text data. |
| Outcome: | The results compare with traditional surveys and a spontaneous word-list generation task. |
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection (2022.naacl-main)
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| Challenge: | XAI features usually provide a single importance score for each token, but feature attribution methods provide two complementary and theoretically-grounded scores for each utterance. |
| Approach: | They propose a feature attribution method that generates explicit perturbations of the input text, allowing the importance scores themselves to be explainable. |
| Outcome: | The proposed method explain the predictions of hate speech detection models on a set of curated examples from a test suite. |
A Swedish Cookie-Theft Corpus (L18-1)
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| Challenge: | Language disturbances can be a diagnostic marker for neurodegenerative diseases, such as Alzheimer's disease, at earlier stages. |
| Approach: | They develop a corpus of audio recordings of the Cookie-theft, a standardized test that has been used in studies in the past. |
| Outcome: | The proposed corpus is based on audio recordings of the Cookie-theft . it provides a rich resource for future research and experimentation in many areas . |
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors (2022.acl-long)
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| Challenge: | a new study shows that general abusive language classifiers are reliable in detecting explicit abuse but fail to detect more subtle abuses. |
| Approach: | They propose an interpretability technique to quantify the sensitivity of a trained model to new data . they propose a degree of explicitness metric to suggest out-of-domain unlabeled examples . |
| Outcome: | The proposed interpretability technique is useful for predicting the generalizability of the model on new data. |
Examining Gender and Racial Bias in Large Vision–Language Models Using a Novel Dataset of Parallel Images (2024.eacl-long)
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| Challenge: | a new wave of large vision–language models (LVLMs) incorporate images as input in addition to text . a recent study examined potential gender and racial biases in such systems based on the perceived characteristics of the people in the input images. |
| Approach: | They examine potential gender and racial biases in large vision–language models . they query a dataset of AI-generated images of people to see whether they differ . |
| Outcome: | The proposed dataset shows that the images differ in gender and race according to the perceived characteristics of the person depicted. |
Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse (2024.emnlp-main)
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| Challenge: | Using large language models, large language model learning has become more integrated into our daily lives, making it increasingly important to ensure they reflect ethical and equitable values. |
| Approach: | They assess how LLMs can apply moral reasoning to both criticize and defend sexist language by evaluating their models and evaluating the moral foundations cited by them. |
| Outcome: | The models show they can provide comprehensible and contextually relevant text for understanding diverse views on how sexism is perceived. |