The central goal of linguistics-the science of human language-is understanding the following: why linguistic expressions are structured the way they are, how these structures represent meaning and externalized signal (sound for spoken languages, manual gestures for signed languages), how humans comprehend and produce these structures, and how humans acquire the mental capacities that make all of this possible (see, among many others, Chomsky, 1965 and Baker, 2002).By discovering the core underlying principles governing language, linguistics aims to 'carve nature at its joints.'Some of these principles may ostensibly be surface false in that they may not capture superficial surface patterns but instead reveal and explain deeper systematic phenomena.Consider, for example, the principle that allows language users to add relative clauses as modifiers of nouns, for example, the bracketed relative clause in the boy [the girl met].The principle, which appears to hold in almost every language, allows us-when stated properly-to replace the girl with any other expression of the same syntactic category, any other noun phrase, for example, the woman.However, this demand, in turn, is not surface true, as observed when considering the psychological difficulties associated with certain so-called center-embedding constructions.So, for example, replacing the girl with a more complex noun phrase such as the girl the woman likes yields a result that a speaker will never use and an addressee will find very difficult to understand: the boy [the girl the woman likes met]. 2 So, we have a fairly general principle with a host of considerations arguing that it is correctly stated, yet it yields results that appear to be false on the surface.The consensus has been that, despite this surface falsity, the principle is, nevertheless, correct in its idealized form (as part of the grammar internalized by a speaker of the language, speaker's competence).The principle appears to be sometimes false, according to this consensus, due to interfering factors (performance factors) pertaining to how speakers make use of the principles of grammar (of their competence) in producing and comprehending sentences in real time.This distinction between competence and performance is none other than the distinction between idealized theory and noise, familiar from high school physics (e.g., the distinction between the theory of motion and friction).And a consequence of this distinction is that there is a notion of 'being correct' for natural languages (being grammatical) that is importantly distinct from the notion of being 'probable' or 'expected.'No such competence-performance distinction (and no distinction between grammaticality and expectedness) is designed into current generative AI systems for language (see Yngve, 1960; Miller & Chomsky, 1963; and Chomsky, 1965; and in the context of LLMs, see Fox & Katzir, 2023; Katzir, 2023; and further in section 2.1).In contrast to some of the earlier works on generative modeling, which were driven by scientific understanding (Rumelhart & McClelland, 1986; Elman, 1993; Hinton, 2007), the development of modern generative AI technologies is largely use or performance driven: generative models of molecules can help scientists discover new drugs, image generation models allow nonexperts to create high-quality graphics at low cost, and LLMs enable computers to perform useful tasks involving language-such as answering a question given a user prompt.These models are generally trained on 'raw' data (e.g., pixels, words) and prioritize prediction over scientific understanding of the data domain.As an example, LLMs make use of enormous neural networks-
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