Papers by Laura Cabello
Being Right for Whose Right Reasons? (2023.acl-long)
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| Challenge: | Existing work has failed to acknowledge that what counts as a rationale is subjective. |
| Approach: | They propose to use demographic annotations to augment existing datasets to ask what demographics our models align with and whose reasoning patterns they align with. |
| Outcome: | The proposed model rationales align better with older and/or white annotators, and are biased towards older and white anorators. |
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance (2024.findings-acl)
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| Challenge: | Existing studies have focused on developing ad hoc models of varying complexity for ABSA subtasks. |
| Approach: | They propose an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on most domains and achieves competitive results on biomedical domain datasets. |
Rather a Nurse than a Physician - Contrastive Explanations under Investigation (2023.emnlp-main)
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| Challenge: | a recent study suggests that contrastive explanations are closer to how humans explain a decision than non-contrastive explanations. |
| Approach: | They analyze four English text-classification datasets to determine whether humans explain in contrast to alternatives. |
| Outcome: | The proposed explanations are closer to how humans explain a decision than non-contrastive explanations. |
Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models (2023.emnlp-main)
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| Challenge: | Pretrained machine learning models perpetuate and even amplify existing biases in data . this can result in unfair outcomes that ultimately impact user experience . |
| Approach: | They quantify bias amplification in pretraining and after fine-tuning on vision-and-language models. |
| Outcome: | The results show that pretrained models can perpetuate and even amplify biases in data without compromising performance. |