Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces

Usashi Chatterjee, Amit Gajbhiye, Steven Schockaert

Submission Type: Regular Short Paper

Submission Track: Semantics: Lexical

Keywords: conceptual spaces, large language models, knowledge representation

Abstract: 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.

Submission Number: 1062