Papers by David Strohmaier
Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs (2025.findings-acl)
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| Challenge: | 211 studies on the demographic representativeness of large language models have conflicting results . 29% of the studies report positive conclusions on the representativeness, 30% do not evaluate LLMs across multiple demographic categories or within demographic subcategories. |
| Approach: | 211 papers review the representativeness of large language models . authors recommend more precise evaluation methods and comprehensive documentation of demographic attributes . |
| Outcome: | 211 studies on the representativeness of large language models are reviewed . 29% of the studies report positive conclusions, but 30% fail to specify subcategories . authors recommend more precise evaluation methods and documentation of demographic attributes . |
SeCoDa: Sense Complexity Dataset (2020.lrec-1)
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| Challenge: | Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for word senses and word tokens. |
| Approach: | They propose to use a hierarchical sense annotation scheme that draws on information available in the Cambridge Advanced Learner's Dictionary to provide more coarse-grained senses than WordNet. |
| Outcome: | The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. |