Browse through our curated collection of machine learning interview questions.
Explain in detail how transformer-based language models, such as GPT, are structured and function. What are the key components involved in their architecture and how do they contribute to the model's ability to understand and generate human language?
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Discuss the differences between fine-tuning and prompt engineering when adapting large language models (LLMs). What are the advantages and disadvantages of each approach, and in what scenarios would you choose one over the other?
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How does the Transformer architecture function in the context of large language models (LLMs) like GPT, and why is it preferred over traditional RNN-based models? Discuss the key components of the Transformer and their roles in processing sequences, especially in NLP tasks.