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Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.03

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Date: 1. 3.2013 14:56:22

Jha, A.: Why GPT-4 isn't all it's cracked up to be (2023) 0.02
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Abstract

They might appear intelligent, but LLMs are nothing of the sort. They don't understand the meanings of the words they are using, nor the concepts expressed within the sentences they create. When asked how to bring a cow back to life, earlier versions of ChatGPT, for example, which ran on a souped-up version of GPT-3, would confidently provide a list of instructions. So-called hallucinations like this happen because language models have no concept of what a "cow" is or that "death" is a non-reversible state of being. LLMs do not have minds that can think about objects in the world and how they relate to each other. All they "know" is how likely it is that some sets of words will follow other sets of words, having calculated those probabilities from their training data. To make sense of all this, I spoke with Gary Marcus, an emeritus professor of psychology and neural science at New York University, for "Babbage", our science and technology podcast. Last year, as the world was transfixed by the sudden appearance of ChatGPT, he made some fascinating predictions about GPT-4.

Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.02

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Date: 22. 3.2015 9:30:24

Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.02

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Date: 29.12.2022 18:22:55

Rieger, F.: Lügende Computer (2023) 0.02

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Date: 16. 3.2023 19:22:55

Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.01

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Date: 22. 1.2018 11:32:44

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