Papers by Nadine Probol
Which Demographics do LLMs Default to During Annotation? (2025.acl-long)
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Johannes Schäfer, Aidan Combs, Christopher Bagdon, Jiahui Li, Nadine Probol, Lynn Greschner, Sean Papay, Yarik Menchaca Resendiz, Aswathy Velutharambath, Amelie Wuehrl, Sabine Weber, Roman Klinger
| Challenge: | Demographics and cultural background of annotators influence the labels they assign in text annotation. |
| Approach: | They examine the attributes of human annotators LLMs inherently mimic and compare them to demographic-conditioned prompts and placebo-conditioned ones. |
| Outcome: | The proposed model incorporates demographics and cultural background into the output of the large language models (LLMs) to evaluate which attributes of human annotators LLMs inherently mimic. |
Autism Detection in Speech – A Survey (2024.findings-eacl)
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| Challenge: | a range of studies have been done on autism in voice, speech and language . females are under-researched in the field, and there are few experiments with transformers . |
| Approach: | They analyse studies of how autism is displayed in voice, speech and language . they define autism and which comorbidities might influence the correct detection . |
| Outcome: | The authors show that there is already a lot of research on autism in speech, but there are still some shortcomings. |