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 dimensions, which essentially correspond to primitive perceptual features (e.g.hue or size).These quality dimensions are usually learned from human judgements, which means that applications of conceptual spaces tend to be limited to narrow domains (e.g.modelling colour or taste).Encouraged by recent findings about the ability of Large Language Models (LLMs) to learn perceptually grounded representations, we explore the potential of such models for learning conceptual spaces.Our experiments show that LLMs can indeed be used for learning meaningful representations to some extent.However, we also find that fine-tuned models of the BERT family are able to match or even outperform the largest GPT-3 model, despite being 2 to 3 orders of magnitude smaller. 1

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 on distilling decontextualised concept embeddings from language models.But concepts can be modelled from different perspectives, whereas concept embeddings typically mostly capture taxonomic structure.To address this issue, we propose a strategy for identifying what different concepts, from a potentially large concept vocabulary, have in common with others.We then represent concepts in terms of the properties they share with the other concepts.To demonstrate the practical usefulness of this way of modelling concepts, we consider the task of ultra-fine entity typing, which is a challenging multi-label classification problem.We show that by augmenting the label set with shared properties, we can improve the performance of the state-of-the-art models for this task. 1