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            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
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    <abstract>Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In this work, we study the implicit quantification and context-sensitivity of generics by leveraging language models as models of language. We create ConGen, a dataset of 2873 naturally occurring generic and quantified sentences in context, and define p-acceptability, a metric based on surprisal that is sensitive to quantification. Our experiments show generics are more context-sensitive than determiner quantifiers and about 20% of naturally occurring generics we analyze express weak generalisations. We also explore how human biases in stereotypes can be observed in language models.</abstract>
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%0 Conference Proceedings
%T Generics are puzzling. Can language models find the missing piece?
%A Cilleruelo, Gustavo
%A Allaway, Emily
%A Haddow, Barry
%A Birch, Alexandra
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F cilleruelo-etal-2025-generics
%X Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In this work, we study the implicit quantification and context-sensitivity of generics by leveraging language models as models of language. We create ConGen, a dataset of 2873 naturally occurring generic and quantified sentences in context, and define p-acceptability, a metric based on surprisal that is sensitive to quantification. Our experiments show generics are more context-sensitive than determiner quantifiers and about 20% of naturally occurring generics we analyze express weak generalisations. We also explore how human biases in stereotypes can be observed in language models.
%U https://aclanthology.org/2025.coling-main.438/
%P 6571-6588

Markdown (Informal)

[Generics are puzzling. Can language models find the missing piece?](https://aclanthology.org/2025.coling-main.438/) (Cilleruelo et al., COLING 2025)

• Generics are puzzling. Can language models find the missing piece? (Cilleruelo et al., COLING 2025)

ACL

• Gustavo Cilleruelo, Emily Allaway, Barry Haddow, and Alexandra Birch. 2025. Generics are puzzling. Can language models find the missing piece?. In Proceedings of the 31st International Conference on Computational Linguistics, pages 6571–6588, Abu Dhabi, UAE. Association for Computational Linguistics.