5 papers
Extracting Conceptual Spaces from LLMs Using Prototype Embeddings
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities and concepts using cognitively meaningful dimensions, typically referring to perceptual features. Such representations are widely used in cogni…
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
Hanane Kteich, Na Li, Usashi Chatterjee +2
Concept embeddings offer a practical and efficient mechanism for injecting commonsense knowledge into downstream tasks. Their core purpose is often not to predict the commonsense p…
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially…
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
Amit Gajbhiye, Zied Bouraoui, Na Li +3
Concepts play a central role in many applications. This includes settings where concepts have to be modelled in the absence of sentence context. Previous work has therefore focused…
Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces
Usashi Chatterjee, Amit Gajbhiye, Steven Schockaert
The theory of Conceptual Spaces is an influential cognitive-linguistic framework for representing the meaning of concepts. Conceptual spaces are constructed from a set of quality d…