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This paper investigates how GPT-4, through its integration with Microsoft’s Copilot, understands the linguistic phenomenon of Aspectual Coercion, particularly in relation to the aspectual verb \'begin\' and the noun \'book\'. The study examines Copilot\'s ability to interpret co-occurrence constraints and the flexibility of semantic interpretations in varying contexts. It highlights the challenges faced by Pre-trained Large Language Models in providing sophisticated linguistic explanations...
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01
Understand the definition of aspectual coercion and its relevance in language processing.
02
Familiarize yourself with GPT-4 architecture and its capabilities.
03
Identify the specific contexts where aspectual coercion applies within your text.
04
Gather examples of sentences that demonstrate aspectual coercion.
05
Utilize GPT-4 to analyze these examples, noting how it handles aspectual variations.
06
Test the model by inputting sentences requiring aspectual coercion and observe the outputs.
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Adjust your inputs based on the model's responses to improve accuracy.

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There is no formal requirement to file anything specifically termed 'aspectual coercion' related to GPT-4. The terms are largely academic or theoretical rather than tied to regulatory filing.
Aspectual coercion helps linguists understand how different aspects of verbs influence sentence meaning. GPT-4 aims to generate coherent and contextually relevant text based on input data.
There is no specific information to report for aspectual coercion or GPT-4 as they do not relate to a formal reporting framework.
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