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.

概念空间理论是一种有影响力的认知语言框架，用于表示概念的意义。通过挖掘大型语言模型（LLM）学习概念空间的潜力，研究发现 BERT 家族的精细调整模型能与甚至胜过最大的 GPT-3 模型，尽管体积要小 2 到 3 个数量级。