Conceptual spaces represent entities in terms of their primitive semantic
features. Such representations are highly valuable but they are notoriously
difficult to learn, especially when it comes to modelling perceptual and
subjective features. Distilling conceptual spaces from Large Language Models
(LLMs) has recently emerged as a promising strategy. However, existing work has
been limited to probing pre-trained LLMs using relatively simple zero-shot
strategies. We focus in particular on the task of ranking entities according to
a given conceptual space dimension. Unfortunately, we cannot directly fine-tune
LLMs on this task, because ground truth rankings for conceptual space
dimensions are rare. We therefore use more readily available features as
training data and analyse whether the ranking capabilities of the resulting
models transfer to perceptual and subjective features. We find that this is
indeed the case, to some extent, but having perceptual and subjective features
in the training data seems essential for achieving the best results. We
furthermore find that pointwise ranking strategies are competitive against
pairwise approaches, in defiance of common wisdom.

通过使用可用的特征为训练数据，并分析这些模型的排名能力是否可以转移到感知和主观特征上，我们发现在一定程度上是可以的，但在训练数据中包含感知和主观特征似乎是实现最佳结果的关键，此外，我们还发现点对点的排名策略与常识相悖，并且能与成对的方法竞争。