Papers with model embeddings
Understanding language-elicited EEG data by predicting it from a fine-tuned language model (N19-1)
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| Challenge: | Existing studies have only found two of the ERPs to be predictable from embeddings of a stream of language. |
| Approach: | They propose to fine tune a language model to predict ERPs by embedding a stream of language into a model that allows them to be more accurate. |
| Outcome: | The proposed model fine tunes the ERPs to predict them for the first time. |
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to reduce disparities in model outcomes have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. |
| Approach: | They propose to use certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. |
| Outcome: | The proposed methods improve equality of odds and equality of opportunity on multiple text classification tasks. |
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 . |