Abstraction is a core principle of Distributional Semantic Models (DSMs) that learn semantic representations for words by applying dimensional reduction to statistical redundancies in language. Although the posited learning mechanisms vary widely, virtually all DSMs are prototype models in that they create a single abstract representation of a word's meaning. This stands in stark contrast to accounts of categorisation that have very much converged on the superiority of exemplar models. However, there is a small but growing group of accounts in psychology, linguistics, and information retrieval that are exemplar-based semantic models. These models borrow many of the ideas that have led to the prominence of exemplar models in fields such as categorisation. Exemplar-based DSMs posit only an episodic store, not a semantic one. Rather than applying abstraction mechanisms at learning, these DSMs posit that semantic abstraction is an emergent artifact of retrieval from episodic memory. [This is the in press version of an article published in "Language, Cognition, and Neuroscience."]