Papers by Marianna Bolognesi

5 papers
Quantifying Generalizations: Exploring the Divide Between Human and LLMs’ Sensitivity to Quantification (2024.acl-long)

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Challenge: Generics are expressions used to communicate abstractions about categories . they allow for exceptions, and they are a powerful way to express knowledge about the world .
Approach: They examine how large language models interpret generics to understand their meanings . they find that the presence of a generic sentence as context influences quantifiers based on the generalization .
Outcome: The proposed models do not exhibit a strong sensitivity to quantification, the study finds . the results suggest that the presence of a generic sentence as context influences quantifiers .
Specifying Genericity through Inclusiveness and Abstractness Continuous Scales (2024.lrec-main)

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Challenge: Using a pilot study, we created a small but crucial annotated dataset of 324 sentences, demonstrating the framework’s effectiveness in capturing nuanced aspects of genericity.
Approach: They propose a framework for fine-grained modeling of noun phrases' genericity in natural language using a small but crucial annotated dataset of 324 sentences.
Outcome: The proposed framework can be used to model genericity of noun phrases in natural language and can be easily compared with existing binary annotations.
Can Large Language Models Interpret Noun-Noun Compounds? A Linguistically-Motivated Study on Lexicalized and Novel Compounds (2024.acl-long)

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Challenge: Noun-noun compounds represent an important challenge for Natural Language Understanding . correct interpretation of noun-nomin compounds is essential for many applications .
Approach: They test whether Large Language Models can interpret the semantic relation between nouns . they also test whether they can abstract from such knowledge to predict the relation .
Outcome: The proposed models can interpret the semantic relation between nouns and compounds using analogical comparisons.
The Contextual Variability of English Nouns: The Impact of Categorical Specificity beyond Conceptual Concreteness (2024.lrec-main)

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Challenge: Empirical studies on conceptual abstraction have examined differences in contextual distributions of abstract and concrete concept words.
Approach: They propose to use a model to investigate the interplay between contextual variability and specificity of abstract and concrete concepts.
Outcome: The proposed models show that more specific words have closer contexts than generic terms.
How Humans and LLMs Organize Conceptual Knowledge: Exploring Subordinate Categories in Italian (2025.acl-long)

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Challenge: Existing studies on hierarchical organization of categories focused on basic-1 . but, words at the subordinate level are crucial for effective communication in specialized domains.
Approach: They analyze a psycholinguistic dataset of human-generated exemplars for 187 concrete words . they then evaluate whether textual and vision LLMs produce meaningful exemplar .
Outcome: The results show that human-generated exemplars perform poorly in three key tasks . the results highlight the potential of using AI-generated categories in psycholinguistic research .

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