Papers by Claudia Collacciani

3 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.

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