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.

翻译：概念空间理论是一种富有影响力的认知语言学框架，用于表征概念的意义。概念空间由一组质量维度构建，这些维度本质上对应原始感知特征（例如色调或大小）。这些质量维度通常从人类判断中习得，这意味着概念空间的应用往往局限于狭窄领域（如颜色或味道建模）。受近期关于大型语言模型（LLMs）能够学习基于感知的表征这一发现的启发，我们探索了此类模型学习概念空间的潜力。实验表明，LLMs确实能在一定程度上用于学习有意义的表征。然而，我们还发现，尽管BERT系列微调模型的参数规模比最大的GPT-3模型小2到3个数量级，但其表现足以匹敌甚至超越后者。