Papers by Roman Rietsche
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 . |
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. |
A Corpus for Suggestion Mining of German Peer Feedback (2022.lrec-1)
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| Challenge: | e.g. Massive Open Online Courses (MOOCs) are increasingly important to meet the demand for feedback in large scale classes. |
| Approach: | They propose to use peer feedback to detect suggestions on how to improve the work of students in a german university course. |
| Outcome: | The proposed corpus is the first student peer feedback corpus in germany and has been labelled with a new annotation scheme. |