In this response to commentators, I agree with those who suggested that the distinction between exemplar- and abstraction-based accounts is something of a false dichotomy and therefore move to an abstractions-made-of-exemplars account under which (a) we store all the exemplars that we hear (subject to attention, decay, interference, etc.) but (b) in the service of language use, re-represent these exemplars at multiple levels of abstraction, as simulated by computational neural-network models such as BERT, ELMo and GPT-3. Whilst I maintain that traditional linguistic abstractions (e.g. a DETERMINER category; SUBJECT VERB OBJECT word order) are no more than human-readable approximations of the type of abstractions formed by both human and artificial multiple-layer networks, I express hope that the abstractions-made-of-exemplars position can point the way towards a truce in the language acquisition wars: We were all right all along, just focusing on different levels of abstraction.

在对评论者的回应中，我同意那些提出的建议，即基于示例的帐户和基于抽象的帐户之间的区别是一种错误的二分法，因此转到了由示例性的抽象帐户，其中（a）我们存储了所有（b）在语言使用服务中，我们听到的示例（受到注意力，衰落，干扰等）（如BERT等计算神经网络模型所模拟）在多个抽象级别上重新表示了这些示例，ELMo和GPT-3。尽管我坚持认为传统的语言抽象（例如DETERMINER类别； SUBJECT VERB OBJECT单词顺序）不过是人类和人工多层网络形成的抽象类型的人类可理解的近似值，