This article examines the philosophical implications of large language models like GPT-4, arguing that their capabilities invoke significant discussions on cognitive competence and language understanding. It serves as an introduction to language models, addressing their historical context, the nature of language understanding, and philosophical issues such as compositionality and nativism. Despite their advancements, the article calls for deeper investigation into the inner workings of these models, setting the groundwork for future research and discussions about their role in artificial intelligence and cognitive science.
The success of language models challenges several long-held assumptions about artificial neural networks, prompting a re-evaluation of our understanding of cognitive competence.
This article serves as a primer on language models for philosophers and surveys their significance in relation to classic debates in cognitive science.
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