Papers by Aidan Combs
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. |
Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts (2025.acl-long)
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| Challenge: | Accurate modeling of subjective phenomena requires data annotated with authors’ intentions. |
| Approach: | They collect study-created and genuine social media posts labeled for emotion and compare them on several dimensions, including model performance. |
| Outcome: | The results show that study-created posts are longer, rely more on text and less on images for emotion expression, and focus more on emotion-prototypical events. |