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Deep Learning and Linguistic Representation

Chapman & Hall/Crc (2021)

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  1. Why is Generative Grammar Recursive?Fintan Mallory - 2023 - Erkenntnis 88 (7):3097-3111.
    A familiar argument goes as follows: natural languages have infinitely many sentences, finite representation of infinite sets requires recursion; therefore any adequate account of linguistic competence will require some kind of recursive device. The first part of this paper argues that this argument is not convincing. The second part argues that it was not the original reason recursive devices were introduced into generative linguistics. The real basis for the use of recursive devices stems from a deeper philosophical concern; a grammar (...)
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  • Assessing the Strengths and Weaknesses of Large Language Models.Shalom Lappin - 2023 - Journal of Logic, Language and Information 33 (1):9-20.
    The transformers that drive chatbots and other AI systems constitute large language models (LLMs). These are currently the focus of a lively discussion in both the scientific literature and the popular media. This discussion ranges from hyperbolic claims that attribute general intelligence and sentience to LLMs, to the skeptical view that these devices are no more than “stochastic parrots”. I present an overview of some of the weak arguments that have been presented against LLMs, and I consider several of the (...)
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  • Are machines radically contextualist?Ryan M. Nefdt - 2023 - Mind and Language 38 (3):750-771.
    In this article, I describe a novel position on the semantics of artificial intelligence. I present a problem for the current artificial neural networks used in machine learning, specifically with relation to natural language tasks. I then propose that from a metasemantic level, meaning in machines can best be interpreted as radically contextualist. Finally, I consider what this might mean for human‐level semantic competence from a comparative perspective.
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