Papers with ConGen

2 papers
Generics are puzzling. Can language models find the missing piece? (2025.coling-main)

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Challenge: Generic sentences express generalisations about the world without explicit quantification . human biases in stereotypes can be observed in language models, authors say .
Approach: They analyze generic sentences to determine their quantification and quantify their implicit quantifications using language models.
Outcome: The proposed model shows that generics are more context-sensitive than determiner quantifiers and express weak generalisations.
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation (2022.findings-emnlp)

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Challenge: Sentence representations are essential in many NLP tasks operating at the sentence level.
Approach: They propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap for smaller models.
Outcome: The proposed method outperforms supervised training on STS, text classification, and natural language inference tasks on smaller models.

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