Papers by Amelie Wuehrl
Which Demographics do LLMs Default to During Annotation? (2025.acl-long)
Copied to clipboard
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. |
What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing studies show that identifying verifiable claims is difficult, whereas identifying unverifiably claims is more challenging. |
| Approach: | They hypothesize that breaking down claims into smaller units increases our understanding which properties impact verifiability. |
| Outcome: | The proposed corpus of evidence is based on the first corpus for scientific fact verification annotated with subject–relation–object triplets, evidence documents, and fact-checking verdicts. |
Understanding Fine-grained Distortions in Reports of Scientific Findings (2024.findings-acl)
Copied to clipboard
| Challenge: | a fine-grained understanding of how scientific findings are reported is crucial, says a new study . a recent study found that tweets distort scientific findings more often than news reports . |
| Approach: | They propose to annotate 1,600 scientific findings from academic papers paired with corresponding tweets . they also establish baselines for automatically detecting these characteristics . |
| Outcome: | The proposed method outperforms few-shot prompting in detecting distortions in unpaired data. |
How Entangled is Factuality and Deception in German? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on deception detection and fact checking conflates factual accuracy with truthfulness . a belief-based deception framework defines texts as deceptive when there is a mismatch between what people say and what they truly believe . |
| Approach: | They assess if presumed patterns of deception generalize to German language texts . they gauge the impact of deceptiveness on the downstream task of fact checking . |
| Outcome: | The proposed framework disentangles deception when there is a mismatch between what people say and what they truly believe . the proposed framework does not find any correlation with established cues of deception . |