Papers by Lynn Greschner
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. |
Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection (2026.eacl-long)
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| Challenge: | Existing computational approaches focus on logical structures of fallacies and argumentation schemes, ignoring the emotional dimension of argumentation. |
| Approach: | They propose to use large language models to systematically change emotional appeals in fallacious arguments by using a computational approach. |
| Outcome: | The proposed method reduces fallacy detection by 14.5% on average on human arguments with enjoyment over fear or sadness. |