Papers by Sabine Weber
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. |
Language Models Are Poor Learners of Directional Inference (2022.findings-emnlp)
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| Challenge: | Existing datasets fail to test directionality and are infested by artefacts that can be learnt as proxy for entailments, yielding over-optimistic results. |
| Approach: | They propose a benchmark for directional predicate entailments that is extrinsic to existing training sets. |
| Outcome: | The proposed model is incompetent on directional predicate entailments, compared to engorgement graphs, but limited by sparsity. |
Cross-lingual Inference with A Chinese Entailment Graph (2022.findings-acl)
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| Challenge: | Existing work on predicate entailment detection from typed open relation triples has not been able to detect predicates. |
| Approach: | They propose a pipeline for building Chinese entailment graphs using an open relation extraction method. |
| Outcome: | The proposed pipeline outperforms monolingual and Chinese entailment graphs on a parallel dataset. |