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。

大型语言模型在学习概念空间中的潜力分析